# Directory Structure
```
├── .env.example
├── .github
│ ├── logo.png
│ └── social-preview.png
├── .gitignore
├── assets
│ ├── apple-touch-icon.png
│ ├── deepseek-dark-mode.png
│ ├── favicon-16x16.png
│ ├── favicon-32x32.png
│ └── favicon.ico
├── Dockerfile
├── LICENSE
├── package-lock.json
├── package.json
├── README.md
├── smithery.yaml
├── src
│ ├── docs
│ │ ├── deepseek-api-example.md
│ │ └── llms-full.txt
│ ├── index.ts
│ └── types.ts
└── tsconfig.json
```
# Files
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
```
node_modules/
build/
.env
*.log
.DS_Store
```
--------------------------------------------------------------------------------
/.env.example:
--------------------------------------------------------------------------------
```
# DeepSeek API Configuration
DEEPSEEK_API_KEY=your-api-key-here
```
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
```markdown
# DeepSeek MCP Server
A Model Context Protocol (MCP) server for the DeepSeek API, allowing seamless integration of DeepSeek's powerful language models with MCP-compatible applications like Claude Desktop.
## *Anonymously* use DeepSeek API -- Only a proxy is seen on the other side
<a href="https://glama.ai/mcp/servers/asht4rqltn"><img width="380" height="200" src="https://glama.ai/mcp/servers/asht4rqltn/badge" alt="DeepSeek Server MCP server" /></a>
[](https://www.npmjs.com/package/deepseek-mcp-server)
[](https://www.npmjs.com/package/deepseek-mcp-server)
[](https://github.com/DMontgomery40/deepseek-mcp-server/issues)
[](https://github.com/DMontgomery40/deepseek-mcp-server/network)
[](https://github.com/DMontgomery40/deepseek-mcp-server/stargazers)
[](https://github.com/DMontgomery40/deepseek-mcp-server/blob/main/LICENSE)
## Installation
### Installing via Smithery
To install DeepSeek MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@dmontgomery40/deepseek-mcp-server):
```bash
npx -y @smithery/cli install @dmontgomery40/deepseek-mcp-server --client claude
```
### Manual Installation
```bash
npm install -g deepseek-mcp-server
```
### Usage with Claude Desktop
Add this to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"deepseek": {
"command": "npx",
"args": [
"-y",
"deepseek-mcp-server"
],
"env": {
"DEEPSEEK_API_KEY": "your-api-key"
}
}
}
}
```
## Features
> Note: The server intelligently handles these natural language requests by mapping them to appropriate configuration changes. You can also query the current settings and available models:
- User: "What models are available?"
- Response: Shows list of available models and their capabilities via the models resource.
- User: "What configuration options do I have?"
- Response: Lists all available configuration options via the model-config resource.
- User: "What is the current temperature setting?"
- Response: Displays the current temperature setting.
- User: "Start a multi-turn conversation. With the following settings: model: 'deepseek-chat', make it not too creative, and
allow 8000 tokens."
- Response: *Starts a multi-turn conversation with the specified settings.*
### Automatic Model Fallback if R1 is down
- If the primary model (R1) is down (called `deepseek-reasoner` in the server), the server will automatically attempt to try with v3 (called `deepseek-chat` in the server)
> Note: You can switch back and forth anytime as well, by just giving your prompt and saying "use `deepseek-reasoner`" or "use `deepseek-chat`"
- V3 is recommended for general purpose use, while R1 is recommended for more technical and complex queries, primarily due to speed and token usage
### Resource discovery for available models and configurations:
* Custom model selection
* Temperature control (0.0 - 2.0)
* Max tokens limit
* Top P sampling (0.0 - 1.0)
* Presence penalty (-2.0 - 2.0)
* Frequency penalty (-2.0 - 2.0)
## Enhanced Conversation Features
**Multi-turn conversation support:**
* Maintains complete message history and context across exchanges
* Preserves configuration settings throughout the conversation
* Handles complex dialogue flows and follow-up chains automatically
This feature is particularly valuable for two key use cases:
1. **Training & Fine-tuning:**
Since DeepSeek is open source, many users are training their own versions. The multi-turn support provides properly formatted conversation data that's essential for training high-quality dialogue models.
2. **Complex Interactions:**
For production use, this helps manage longer conversations where context is crucial:
* Multi-step reasoning problems
* Interactive troubleshooting sessions
* Detailed technical discussions
* Any scenario where context from earlier messages impacts later responses
The implementation handles all context management and message formatting behind the scenes, letting you focus on the actual interaction rather than the technical details of maintaining conversation state.
## Testing with MCP Inspector
You can test the server locally using the MCP Inspector tool:
1. Build the server:
```bash
npm run build
```
2. Run the server with MCP Inspector:
```bash
# Make sure to specify the full path to the built server
npx @modelcontextprotocol/inspector node ./build/index.js
```
The inspector will open in your browser and connect to the server via stdio transport. You can:
- View available tools
- Test chat completions with different parameters
- Debug server responses
- Monitor server performance
Note: The server uses DeepSeek's R1 model (deepseek-reasoner) by default, which provides state-of-the-art performance for reasoning and general tasks.
## License
MIT
```
--------------------------------------------------------------------------------
/tsconfig.json:
--------------------------------------------------------------------------------
```json
{
"compilerOptions": {
"target": "ES2020",
"module": "ES2020",
"moduleResolution": "node",
"outDir": "./build",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true
},
"include": ["src/**/*"],
"exclude": ["node_modules"]
}
```
--------------------------------------------------------------------------------
/smithery.yaml:
--------------------------------------------------------------------------------
```yaml
# Smithery configuration file: https://smithery.ai/docs/config#smitheryyaml
startCommand:
type: stdio
configSchema:
# JSON Schema defining the configuration options for the MCP.
type: object
required:
- deepseekApiKey
properties:
deepseekApiKey:
type: string
description: The API key for the DeepSeek server.
commandFunction:
# A function that produces the CLI command to start the MCP on stdio.
|-
(config) => ({ command: 'node', args: ['build/index.js'], env: { DEEPSEEK_API_KEY: config.deepseekApiKey } })
```
--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
```dockerfile
# Generated by https://smithery.ai. See: https://smithery.ai/docs/config#dockerfile
FROM node:18-alpine AS builder
WORKDIR /app
# Copy the necessary files
COPY package.json package-lock.json ./
COPY tsconfig.json ./
COPY src ./src
# Install dependencies and build the project
RUN npm install && npm run build
# Use a smaller base image for the release
FROM node:18-alpine AS release
WORKDIR /app
# Copy only the built output and necessary files
COPY --from=builder /app/build ./build
COPY --from=builder /app/package.json ./package.json
COPY --from=builder /app/package-lock.json ./package-lock.json
# Install production dependencies only
RUN npm install --production
# Set environment variable
ENV NODE_ENV=production
# Entry point
ENTRYPOINT ["node", "build/index.js"]
```
--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
```json
{
"name": "deepseek-mcp-server",
"version": "0.2.1",
"description": "Model Context Protocol server for DeepSeek's advanced language models.",
"type": "module",
"main": "build/index.js",
"bin": {
"deepseek-mcp-server": "./build/index.js"
},
"files": [
"build/**/*",
"assets/favicon.ico",
"assets/favicon-32x32.png",
"assets/apple-touch-icon.png"
],
"scripts": {
"build": "tsc",
"start": "node build/index.js",
"prepublishOnly": "npm run build",
"inspect": "npx @modelcontextprotocol/inspector build/index.js"
},
"dependencies": {
"@modelcontextprotocol/sdk": "latest",
"axios": "^1.6.0",
"dotenv": "^16.3.1"
},
"devDependencies": {
"@types/node": "^20.8.0",
"typescript": "^5.2.2"
},
"author": "DMontgomery40",
"license": "MIT",
"repository": {
"type": "git",
"url": "https://github.com/DMontgomery40/deepseek-mcp-server"
},
"keywords": [
"mcp",
"deepseek",
"ai",
"llm"
]
}
```
--------------------------------------------------------------------------------
/src/types.ts:
--------------------------------------------------------------------------------
```typescript
export interface DeepSeekResponse {
id: string;
object: string;
created: number;
model: string;
choices: Array<{
index: number;
message: {
role: 'assistant';
content: string;
};
finish_reason: 'stop' | 'length' | 'content_filter';
}>;
usage: {
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
};
}
export interface ChatMessage {
role: 'system' | 'user' | 'assistant';
content: string;
}
export interface ChatCompletionArgs {
message?: string;
messages?: ChatMessage[];
model?: string;
temperature?: number;
max_tokens?: number;
top_p?: number;
frequency_penalty?: number;
presence_penalty?: number;
}
// Type guard for chat completion arguments
export interface ModelInfo {
id: string;
name: string;
description: string;
}
export interface ModelConfig {
id: string;
name: string;
type: 'number' | 'integer' | 'string' | 'boolean';
description: string;
default?: any;
minimum?: number;
maximum?: number;
}
export function isValidChatCompletionArgs(args: unknown): args is ChatCompletionArgs {
if (!args || typeof args !== 'object') {
return false;
}
const { message, messages, model, temperature, max_tokens, top_p, frequency_penalty, presence_penalty } = args as ChatCompletionArgs;
// Must have either message or messages
if (!message && !messages) {
return false;
}
// If message is provided, it must be a string
if (message !== undefined && typeof message !== 'string') {
return false;
}
// If messages is provided, validate the array
if (messages !== undefined) {
if (!Array.isArray(messages)) {
return false;
}
const validRoles = ['system', 'user', 'assistant'] as const;
const isValidMessage = (msg: unknown): msg is ChatMessage => {
if (!msg || typeof msg !== 'object') {
return false;
}
const { role, content } = msg as ChatMessage;
return (
validRoles.includes(role as typeof validRoles[number]) &&
typeof content === 'string'
);
};
if (!messages.every(isValidMessage)) {
return false;
}
}
if (model !== undefined && typeof model !== 'string') {
return false;
}
if (temperature !== undefined && (typeof temperature !== 'number' || temperature < 0 || temperature > 2)) {
return false;
}
if (max_tokens !== undefined && (typeof max_tokens !== 'number' || max_tokens < 1)) {
return false;
}
if (top_p !== undefined && (typeof top_p !== 'number' || top_p < 0 || top_p > 1)) {
return false;
}
if (frequency_penalty !== undefined && (typeof frequency_penalty !== 'number' || frequency_penalty < -2 || frequency_penalty > 2)) {
return false;
}
if (presence_penalty !== undefined && (typeof presence_penalty !== 'number' || presence_penalty < -2 || presence_penalty > 2)) {
return false;
}
return true;
}
```
--------------------------------------------------------------------------------
/src/docs/deepseek-api-example.md:
--------------------------------------------------------------------------------
```markdown
Reasoning Model (deepseek-reasoner)
deepseek-reasoner is a reasoning model developed by DeepSeek. Before delivering the final answer, the model first generates a Chain of Thought (CoT) to enhance the accuracy of its responses. Our API provides users with access to the CoT content generated by deepseek-reasoner, enabling them to view, display, and distill it.
When using deepseek-reasoner, please upgrade the OpenAI SDK first to support the new parameters.
pip3 install -U openai
API Parameters
Input：
max_tokens：The maximum length of the final response after the CoT output is completed, defaulting to 4K, with a maximum of 8K. Note that the CoT output can reach up to 32K tokens, and the parameter to control the CoT length (reasoning_effort) will be available soon.
Output：
reasoning_content：The content of the CoT，which is at the same level as content in the output structure. See API Example for details
contentThe content of the final answer
Context Length：The API supports a maximum context length of 64K, and the length of the output reasoning_content is not counted within the 64K context length.
Supported Features：Chat Completion、Chat Prefix Completion (Beta)
Not Supported Features：Function Call、Json Output、FIM (Beta)
Not Supported Parameters：temperature、top_p、presence_penalty、frequency_penalty、logprobs、top_logprobs. Please note that to ensure compatibility with existing software, setting these parameters will not trigger an error but will also have no effect.
Multi-round Conversation
In each round of the conversation, the model outputs the CoT (reasoning_content) and the final answer (content). In the next round of the conversation, the CoT from previous rounds is not concatenated into the context, as illustrated in the following diagram:
Please note that if the reasoning_content field is included in the sequence of input messages, the API will return a 400 error. Therefore, you should remove the reasoning_content field from the API response before making the API request, as demonstrated in the API example.
API Example
The following code, using Python as an example, demonstrates how to access the CoT and the final answer, as well as how to conduct multi-round conversations:
NoStreaming
Streaming
from openai import OpenAI
client = OpenAI(api_key="<DeepSeek API Key>", base_url="https://api.deepseek.com")
# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
model="deepseek-reasoner",
messages=messages
)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
# Round 2
messages.append({'role': 'assistant', 'content': content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
model="deepseek-reasoner",
messages=messages
)
# ...
Previous
Get User Balance
Next
Multi-round Conversation
API Parameters
Multi-round Conversation
API Example
```
--------------------------------------------------------------------------------
/src/index.ts:
--------------------------------------------------------------------------------
```typescript
#!/usr/bin/env node
import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import axios, { AxiosInstance, AxiosResponse, AxiosError } from 'axios';
import dotenv from "dotenv";
import {
DeepSeekResponse,
ChatMessage,
ModelInfo,
ModelConfig
} from "./types.js";
dotenv.config();
const API_KEY = process.env.DEEPSEEK_API_KEY;
if (!API_KEY) {
throw new Error("DEEPSEEK_API_KEY environment variable is required");
}
const API_CONFIG = {
BASE_URL: 'https://api.deepseek.com/v1',
DEFAULT_MODEL: 'deepseek-reasoner',
ENDPOINTS: {
CHAT: '/chat/completions'
}
} as const;
const MODELS: ModelInfo[] = [
{
id: "deepseek-chat",
name: "DeepSeek Chat",
description: "General-purpose chat model optimized for dialogue"
},
{
id: "deepseek-reasoner",
name: "DeepSeek Reasoner",
description: "Model optimized for reasoning and problem-solving"
}
];
const MODEL_CONFIGS: ModelConfig[] = [
{
id: "temperature",
name: "Temperature",
type: "number",
description: "Controls randomness in the output (0.0 to 2.0)",
default: 0.7,
minimum: 0,
maximum: 2
},
{
id: "max_tokens",
name: "Maximum Tokens",
type: "integer",
description: "Maximum number of tokens to generate",
default: 8000,
minimum: 1
},
{
id: "top_p",
name: "Top P",
type: "number",
description: "Controls diversity via nucleus sampling (0.0 to 1.0)",
default: 1.0,
minimum: 0,
maximum: 1
},
{
id: "frequency_penalty",
name: "Frequency Penalty",
type: "number",
description: "Reduces repetition by penalizing frequent tokens (-2.0 to 2.0)",
default: 0.1,
minimum: -2,
maximum: 2
},
{
id: "presence_penalty",
name: "Presence Penalty",
type: "number",
description: "Reduces repetition by penalizing used tokens (-2.0 to 2.0)",
default: 0,
minimum: -2,
maximum: 2
}
];
class DeepSeekServer {
private server: McpServer;
private axiosInstance: AxiosInstance;
private conversationHistory: ChatMessage[] = [];
constructor() {
this.server = new McpServer({
name: "deepseek-mcp-server",
version: "0.1.0"
});
this.axiosInstance = axios.create({
baseURL: API_CONFIG.BASE_URL,
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
}
});
// Set up error handling first
this.setupErrorHandling();
// Then set up resources and tools
this.setupResources();
this.setupTools();
}
private setupErrorHandling(): void {
// Handle API errors
this.axiosInstance.interceptors.response.use(
(response: AxiosResponse) => response,
(error: AxiosError) => {
console.error("[API Error]", error.response?.data || error.message);
throw error;
}
);
// Handle process termination
process.on('SIGINT', async () => {
console.error("Shutting down...");
await this.server.close();
process.exit(0);
});
// Handle uncaught exceptions
process.on('uncaughtException', (error) => {
console.error("[Uncaught Exception]", error);
process.exit(1);
});
}
private setupResources(): void {
// Models resource
this.server.resource(
"models",
new ResourceTemplate("models://{modelId}", {
list: async () => ({
resources: MODELS.map(model => ({
uri: `models://${model.id}`,
name: model.name,
description: model.description
}))
})
}),
async (uri, { modelId }) => ({
contents: [{
uri: uri.href,
text: JSON.stringify(MODELS.find(m => m.id === modelId), null, 2)
}]
})
);
// Model config resource
this.server.resource(
"model-config",
new ResourceTemplate("config://{modelId}", {
list: async () => ({
resources: MODEL_CONFIGS.map(config => ({
uri: `config://${config.id}`,
name: config.name,
description: config.description
}))
})
}),
async (uri, { modelId }) => ({
contents: MODEL_CONFIGS.map(config => ({
uri: `config://${modelId}/${config.id}`,
text: JSON.stringify(config, null, 2)
}))
})
);
}
private setupTools(): void {
// Chat completion tool
this.server.tool(
"chat_completion",
{
message: z.string().optional(),
messages: z.array(z.object({
role: z.enum(['system', 'user', 'assistant']),
content: z.string()
})).optional(),
model: z.string().default('deepseek-reasoner'),
temperature: z.number().min(0).max(2).default(0.7),
max_tokens: z.number().positive().int().default(8000),
top_p: z.number().min(0).max(1).default(1.0),
frequency_penalty: z.number().min(-2).max(2).default(0.1),
presence_penalty: z.number().min(-2).max(2).default(0)
},
async (args) => {
let messages: ChatMessage[];
if (args.message) {
messages = [{ role: 'user', content: args.message }];
} else if (args.messages) {
messages = args.messages;
} else {
throw new Error("Either 'message' or 'messages' must be provided");
}
try {
const response = await this.axiosInstance.post<DeepSeekResponse>(
API_CONFIG.ENDPOINTS.CHAT,
{
messages,
model: args.model,
temperature: args.temperature,
max_tokens: args.max_tokens,
top_p: args.top_p,
frequency_penalty: args.frequency_penalty,
presence_penalty: args.presence_penalty
}
);
return {
content: [{
type: "text",
text: response.data.choices[0].message.content
}]
};
} catch (error) {
console.error("Error with deepseek-reasoner, falling back to deepseek-chat");
try {
const fallbackResponse = await this.axiosInstance.post<DeepSeekResponse>(
API_CONFIG.ENDPOINTS.CHAT,
{
messages,
model: 'deepseek-chat',
temperature: args.temperature,
max_tokens: args.max_tokens,
top_p: args.top_p,
frequency_penalty: args.frequency_penalty,
presence_penalty: args.presence_penalty
}
);
return {
content: [{
type: "text",
text: "Note: Fallback to deepseek-chat due to reasoner error.\n\n" +
fallbackResponse.data.choices[0].message.content
}]
};
} catch (fallbackError) {
if (axios.isAxiosError(fallbackError)) {
throw new Error(`DeepSeek API error: ${fallbackError.response?.data?.error?.message ?? fallbackError.message}`);
}
throw fallbackError;
}
}
}
);
// Multi-turn chat tool
this.server.tool(
"multi_turn_chat",
{
messages: z.union([
z.string(),
z.array(z.object({
role: z.enum(['system', 'user', 'assistant']),
content: z.object({
type: z.literal('text'),
text: z.string()
})
}))
]).transform(messages => {
if (typeof messages === 'string') {
return [{
role: 'user' as const,
content: {
type: 'text' as const,
text: messages
}
}];
}
return messages;
}),
model: z.string().default('deepseek-chat'),
temperature: z.number().min(0).max(2).default(0.7),
max_tokens: z.number().positive().int().default(8000),
top_p: z.number().min(0).max(1).default(1.0),
frequency_penalty: z.number().min(-2).max(2).default(0.1),
presence_penalty: z.number().min(-2).max(2).default(0)
},
async (args) => {
try {
// Transform new messages
const newMessage = args.messages[0];
const transformedNewMessage = {
role: newMessage.role,
content: newMessage.content.text
};
// Add new message to history
this.conversationHistory.push(transformedNewMessage);
// Transform all messages for API
const transformedMessages = this.conversationHistory.map(msg => ({
role: msg.role,
content: msg.content
}));
const response = await this.axiosInstance.post<DeepSeekResponse>(
API_CONFIG.ENDPOINTS.CHAT,
{
messages: transformedMessages,
model: args.model,
temperature: args.temperature,
max_tokens: args.max_tokens,
top_p: args.top_p,
frequency_penalty: args.frequency_penalty,
presence_penalty: args.presence_penalty
}
);
// Add assistant's response to history
const assistantMessage = {
role: 'assistant' as const,
content: response.data.choices[0].message.content
};
this.conversationHistory.push(assistantMessage);
return {
content: [{
type: "text",
text: assistantMessage.content
}]
};
} catch (error) {
if (axios.isAxiosError(error)) {
throw new Error(`DeepSeek API error: ${error.response?.data?.error?.message ?? error.message}`);
}
throw error;
}
}
);
}
async run(): Promise<void> {
const transport = new StdioServerTransport();
await this.server.connect(transport);
console.error("DeepSeek MCP server running on stdio");
}
}
const server = new DeepSeekServer();
server.run().catch(console.error);
```
--------------------------------------------------------------------------------
/src/docs/llms-full.txt:
--------------------------------------------------------------------------------
```
# Clients
A list of applications that support MCP integrations
This page provides an overview of applications that support the Model Context Protocol (MCP). Each client may support different MCP features, allowing for varying levels of integration with MCP servers.
## Feature support matrix
| Client | [Resources] | [Prompts] | [Tools] | [Sampling] | Roots | Notes |
| ---------------------------- | ----------- | --------- | ------- | ---------- | ----- | ------------------------------------------------ |
| [Claude Desktop App][Claude] | ✅ | ✅ | ✅ | ❌ | ❌ | Full support for all MCP features |
| [Zed][Zed] | ❌ | ✅ | ❌ | ❌ | ❌ | Prompts appear as slash commands |
| [Sourcegraph Cody][Cody] | ✅ | ❌ | ❌ | ❌ | ❌ | Supports resources through OpenCTX |
| [Firebase Genkit][Genkit] | ⚠️ | ✅ | ✅ | ❌ | ❌ | Supports resource list and lookup through tools. |
| [Continue][Continue] | ✅ | ✅ | ✅ | ❌ | ❌ | Full support for all MCP features |
[Claude]: https://claude.ai/download
[Zed]: https://zed.dev
[Cody]: https://sourcegraph.com/cody
[Genkit]: https://github.com/firebase/genkit
[Continue]: https://github.com/continuedev/continue
[Resources]: https://modelcontextprotocol.io/docs/concepts/resources
[Prompts]: https://modelcontextprotocol.io/docs/concepts/prompts
[Tools]: https://modelcontextprotocol.io/docs/concepts/tools
[Sampling]: https://modelcontextprotocol.io/docs/concepts/sampling
## Client details
### Claude Desktop App
The Claude desktop application provides comprehensive support for MCP, enabling deep integration with local tools and data sources.
**Key features:**
* Full support for resources, allowing attachment of local files and data
* Support for prompt templates
* Tool integration for executing commands and scripts
* Local server connections for enhanced privacy and security
> ⓘ Note: The Claude.ai web application does not currently support MCP. MCP features are only available in the desktop application.
### Zed
[Zed](https://zed.dev/docs/assistant/model-context-protocol) is a high-performance code editor with built-in MCP support, focusing on prompt templates and tool integration.
**Key features:**
* Prompt templates surface as slash commands in the editor
* Tool integration for enhanced coding workflows
* Tight integration with editor features and workspace context
* Does not support MCP resources
### Sourcegraph Cody
[Cody](https://openctx.org/docs/providers/modelcontextprotocol) is Sourcegraph's AI coding assistant, which implements MCP through OpenCTX.
**Key features:**
* Support for MCP resources
* Integration with Sourcegraph's code intelligence
* Uses OpenCTX as an abstraction layer
* Future support planned for additional MCP features
### Firebase Genkit
[Genkit](https://github.com/firebase/genkit) is Firebase's SDK for building and integrating GenAI features into applications. The [genkitx-mcp](https://github.com/firebase/genkit/tree/main/js/plugins/mcp) plugin enables consuming MCP servers as a client or creating MCP servers from Genkit tools and prompts.
**Key features:**
* Client support for tools and prompts (resources partially supported)
* Rich discovery with support in Genkit's Dev UI playground
* Seamless interoperability with Genkit's existing tools and prompts
* Works across a wide variety of GenAI models from top providers
### Continue
[Continue](https://github.com/continuedev/continue) is an open-source AI code assistant, with built-in support for all MCP features.
**Key features**
* Type "@" to mention MCP resources
* Prompt templates surface as slash commands
* Use both built-in and MCP tools directly in chat
* Supports VS Code and JetBrains IDEs, with any LLM
## Adding MCP support to your application
If you've added MCP support to your application, we encourage you to submit a pull request to add it to this list. MCP integration can provide your users with powerful contextual AI capabilities and make your application part of the growing MCP ecosystem.
Benefits of adding MCP support:
* Enable users to bring their own context and tools
* Join a growing ecosystem of interoperable AI applications
* Provide users with flexible integration options
* Support local-first AI workflows
To get started with implementing MCP in your application, check out our [Python](https://github.com/modelcontextprotocol/python-sdk) or [TypeScript SDK Documentation](https://github.com/modelcontextprotocol/typescript-sdk)
## Updates and corrections
This list is maintained by the community. If you notice any inaccuracies or would like to update information about MCP support in your application, please submit a pull request or [open an issue in our documentation repository](https://github.com/modelcontextprotocol/docs/issues).
# Core architecture
Understand how MCP connects clients, servers, and LLMs
The Model Context Protocol (MCP) is built on a flexible, extensible architecture that enables seamless communication between LLM applications and integrations. This document covers the core architectural components and concepts.
## Overview
MCP follows a client-server architecture where:
* **Hosts** are LLM applications (like Claude Desktop or IDEs) that initiate connections
* **Clients** maintain 1:1 connections with servers, inside the host application
* **Servers** provide context, tools, and prompts to clients
```mermaid
flowchart LR
subgraph " Host (e.g., Claude Desktop) "
client1[MCP Client]
client2[MCP Client]
end
subgraph "Server Process"
server1[MCP Server]
end
subgraph "Server Process"
server2[MCP Server]
end
client1 <-->|Transport Layer| server1
client2 <-->|Transport Layer| server2
```
## Core components
### Protocol layer
The protocol layer handles message framing, request/response linking, and high-level communication patterns.
<Tabs>
<Tab title="TypeScript">
```typescript
class Protocol<Request, Notification, Result> {
// Handle incoming requests
setRequestHandler<T>(schema: T, handler: (request: T, extra: RequestHandlerExtra) => Promise<Result>): void
// Handle incoming notifications
setNotificationHandler<T>(schema: T, handler: (notification: T) => Promise<void>): void
// Send requests and await responses
request<T>(request: Request, schema: T, options?: RequestOptions): Promise<T>
// Send one-way notifications
notification(notification: Notification): Promise<void>
}
```
</Tab>
<Tab title="Python">
```python
class Session(BaseSession[RequestT, NotificationT, ResultT]):
async def send_request(
self,
request: RequestT,
result_type: type[Result]
) -> Result:
"""
Send request and wait for response. Raises McpError if response contains error.
"""
# Request handling implementation
async def send_notification(
self,
notification: NotificationT
) -> None:
"""Send one-way notification that doesn't expect response."""
# Notification handling implementation
async def _received_request(
self,
responder: RequestResponder[ReceiveRequestT, ResultT]
) -> None:
"""Handle incoming request from other side."""
# Request handling implementation
async def _received_notification(
self,
notification: ReceiveNotificationT
) -> None:
"""Handle incoming notification from other side."""
# Notification handling implementation
```
</Tab>
</Tabs>
Key classes include:
* `Protocol`
* `Client`
* `Server`
### Transport layer
The transport layer handles the actual communication between clients and servers. MCP supports multiple transport mechanisms:
1. **Stdio transport**
* Uses standard input/output for communication
* Ideal for local processes
2. **HTTP with SSE transport**
* Uses Server-Sent Events for server-to-client messages
* HTTP POST for client-to-server messages
All transports use [JSON-RPC](https://www.jsonrpc.org/) 2.0 to exchange messages. See the [specification](https://spec.modelcontextprotocol.io) for detailed information about the Model Context Protocol message format.
### Message types
MCP has these main types of messages:
1. **Requests** expect a response from the other side:
```typescript
interface Request {
method: string;
params?: { ... };
}
```
2. **Notifications** are one-way messages that don't expect a response:
```typescript
interface Notification {
method: string;
params?: { ... };
}
```
3. **Results** are successful responses to requests:
```typescript
interface Result {
[key: string]: unknown;
}
```
4. **Errors** indicate that a request failed:
```typescript
interface Error {
code: number;
message: string;
data?: unknown;
}
```
## Connection lifecycle
### 1. Initialization
```mermaid
sequenceDiagram
participant Client
participant Server
Client->>Server: initialize request
Server->>Client: initialize response
Client->>Server: initialized notification
Note over Client,Server: Connection ready for use
```
1. Client sends `initialize` request with protocol version and capabilities
2. Server responds with its protocol version and capabilities
3. Client sends `initialized` notification as acknowledgment
4. Normal message exchange begins
### 2. Message exchange
After initialization, the following patterns are supported:
* **Request-Response**: Client or server sends requests, the other responds
* **Notifications**: Either party sends one-way messages
### 3. Termination
Either party can terminate the connection:
* Clean shutdown via `close()`
* Transport disconnection
* Error conditions
## Error handling
MCP defines these standard error codes:
```typescript
enum ErrorCode {
// Standard JSON-RPC error codes
ParseError = -32700,
InvalidRequest = -32600,
MethodNotFound = -32601,
InvalidParams = -32602,
InternalError = -32603
}
```
SDKs and applications can define their own error codes above -32000.
Errors are propagated through:
* Error responses to requests
* Error events on transports
* Protocol-level error handlers
## Implementation example
Here's a basic example of implementing an MCP server:
<Tabs>
<Tab title="TypeScript">
```typescript
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
const server = new Server({
name: "example-server",
version: "1.0.0"
}, {
capabilities: {
resources: {}
}
});
// Handle requests
server.setRequestHandler(ListResourcesRequestSchema, async () => {
return {
resources: [
{
uri: "example://resource",
name: "Example Resource"
}
]
};
});
// Connect transport
const transport = new StdioServerTransport();
await server.connect(transport);
```
</Tab>
<Tab title="Python">
```python
import asyncio
import mcp.types as types
from mcp.server import Server
from mcp.server.stdio import stdio_server
app = Server("example-server")
@app.list_resources()
async def list_resources() -> list[types.Resource]:
return [
types.Resource(
uri="example://resource",
name="Example Resource"
)
]
async def main():
async with stdio_server() as streams:
await app.run(
streams[0],
streams[1],
app.create_initialization_options()
)
if __name__ == "__main__":
asyncio.run(main)
```
</Tab>
</Tabs>
## Best practices
### Transport selection
1. **Local communication**
* Use stdio transport for local processes
* Efficient for same-machine communication
* Simple process management
2. **Remote communication**
* Use SSE for scenarios requiring HTTP compatibility
* Consider security implications including authentication and authorization
### Message handling
1. **Request processing**
* Validate inputs thoroughly
* Use type-safe schemas
* Handle errors gracefully
* Implement timeouts
2. **Progress reporting**
* Use progress tokens for long operations
* Report progress incrementally
* Include total progress when known
3. **Error management**
* Use appropriate error codes
* Include helpful error messages
* Clean up resources on errors
## Security considerations
1. **Transport security**
* Use TLS for remote connections
* Validate connection origins
* Implement authentication when needed
2. **Message validation**
* Validate all incoming messages
* Sanitize inputs
* Check message size limits
* Verify JSON-RPC format
3. **Resource protection**
* Implement access controls
* Validate resource paths
* Monitor resource usage
* Rate limit requests
4. **Error handling**
* Don't leak sensitive information
* Log security-relevant errors
* Implement proper cleanup
* Handle DoS scenarios
## Debugging and monitoring
1. **Logging**
* Log protocol events
* Track message flow
* Monitor performance
* Record errors
2. **Diagnostics**
* Implement health checks
* Monitor connection state
* Track resource usage
* Profile performance
3. **Testing**
* Test different transports
* Verify error handling
* Check edge cases
* Load test servers
# Prompts
Create reusable prompt templates and workflows
Prompts enable servers to define reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.
<Note>
Prompts are designed to be **user-controlled**, meaning they are exposed from servers to clients with the intention of the user being able to explicitly select them for use.
</Note>
## Overview
Prompts in MCP are predefined templates that can:
* Accept dynamic arguments
* Include context from resources
* Chain multiple interactions
* Guide specific workflows
* Surface as UI elements (like slash commands)
## Prompt structure
Each prompt is defined with:
```typescript
{
name: string; // Unique identifier for the prompt
description?: string; // Human-readable description
arguments?: [ // Optional list of arguments
{
name: string; // Argument identifier
description?: string; // Argument description
required?: boolean; // Whether argument is required
}
]
}
```
## Discovering prompts
Clients can discover available prompts through the `prompts/list` endpoint:
```typescript
// Request
{
method: "prompts/list"
}
// Response
{
prompts: [
{
name: "analyze-code",
description: "Analyze code for potential improvements",
arguments: [
{
name: "language",
description: "Programming language",
required: true
}
]
}
]
}
```
## Using prompts
To use a prompt, clients make a `prompts/get` request:
````typescript
// Request
{
method: "prompts/get",
params: {
name: "analyze-code",
arguments: {
language: "python"
}
}
}
// Response
{
description: "Analyze Python code for potential improvements",
messages: [
{
role: "user",
content: {
type: "text",
text: "Please analyze the following Python code for potential improvements:\n\n```python\ndef calculate_sum(numbers):\n total = 0\n for num in numbers:\n total = total + num\n return total\n\nresult = calculate_sum([1, 2, 3, 4, 5])\nprint(result)\n```"
}
}
]
}
````
## Dynamic prompts
Prompts can be dynamic and include:
### Embedded resource context
```json
{
"name": "analyze-project",
"description": "Analyze project logs and code",
"arguments": [
{
"name": "timeframe",
"description": "Time period to analyze logs",
"required": true
},
{
"name": "fileUri",
"description": "URI of code file to review",
"required": true
}
]
}
```
When handling the `prompts/get` request:
```json
{
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": "Analyze these system logs and the code file for any issues:"
}
},
{
"role": "user",
"content": {
"type": "resource",
"resource": {
"uri": "logs://recent?timeframe=1h",
"text": "[2024-03-14 15:32:11] ERROR: Connection timeout in network.py:127\n[2024-03-14 15:32:15] WARN: Retrying connection (attempt 2/3)\n[2024-03-14 15:32:20] ERROR: Max retries exceeded",
"mimeType": "text/plain"
}
}
},
{
"role": "user",
"content": {
"type": "resource",
"resource": {
"uri": "file:///path/to/code.py",
"text": "def connect_to_service(timeout=30):\n retries = 3\n for attempt in range(retries):\n try:\n return establish_connection(timeout)\n except TimeoutError:\n if attempt == retries - 1:\n raise\n time.sleep(5)\n\ndef establish_connection(timeout):\n # Connection implementation\n pass",
"mimeType": "text/x-python"
}
}
}
]
}
```
### Multi-step workflows
```typescript
const debugWorkflow = {
name: "debug-error",
async getMessages(error: string) {
return [
{
role: "user",
content: {
type: "text",
text: `Here's an error I'm seeing: ${error}`
}
},
{
role: "assistant",
content: {
type: "text",
text: "I'll help analyze this error. What have you tried so far?"
}
},
{
role: "user",
content: {
type: "text",
text: "I've tried restarting the service, but the error persists."
}
}
];
}
};
```
## Example implementation
Here's a complete example of implementing prompts in an MCP server:
<Tabs>
<Tab title="TypeScript">
```typescript
import { Server } from "@modelcontextprotocol/sdk/server";
import {
ListPromptsRequestSchema,
GetPromptRequestSchema
} from "@modelcontextprotocol/sdk/types";
const PROMPTS = {
"git-commit": {
name: "git-commit",
description: "Generate a Git commit message",
arguments: [
{
name: "changes",
description: "Git diff or description of changes",
required: true
}
]
},
"explain-code": {
name: "explain-code",
description: "Explain how code works",
arguments: [
{
name: "code",
description: "Code to explain",
required: true
},
{
name: "language",
description: "Programming language",
required: false
}
]
}
};
const server = new Server({
name: "example-prompts-server",
version: "1.0.0"
}, {
capabilities: {
prompts: {}
}
});
// List available prompts
server.setRequestHandler(ListPromptsRequestSchema, async () => {
return {
prompts: Object.values(PROMPTS)
};
});
// Get specific prompt
server.setRequestHandler(GetPromptRequestSchema, async (request) => {
const prompt = PROMPTS[request.params.name];
if (!prompt) {
throw new Error(`Prompt not found: ${request.params.name}`);
}
if (request.params.name === "git-commit") {
return {
messages: [
{
role: "user",
content: {
type: "text",
text: `Generate a concise but descriptive commit message for these changes:\n\n${request.params.arguments?.changes}`
}
}
]
};
}
if (request.params.name === "explain-code") {
const language = request.params.arguments?.language || "Unknown";
return {
messages: [
{
role: "user",
content: {
type: "text",
text: `Explain how this ${language} code works:\n\n${request.params.arguments?.code}`
}
}
]
};
}
throw new Error("Prompt implementation not found");
});
```
</Tab>
<Tab title="Python">
```python
from mcp.server import Server
import mcp.types as types
# Define available prompts
PROMPTS = {
"git-commit": types.Prompt(
name="git-commit",
description="Generate a Git commit message",
arguments=[
types.PromptArgument(
name="changes",
description="Git diff or description of changes",
required=True
)
],
),
"explain-code": types.Prompt(
name="explain-code",
description="Explain how code works",
arguments=[
types.PromptArgument(
name="code",
description="Code to explain",
required=True
),
types.PromptArgument(
name="language",
description="Programming language",
required=False
)
],
)
}
# Initialize server
app = Server("example-prompts-server")
@app.list_prompts()
async def list_prompts() -> list[types.Prompt]:
return list(PROMPTS.values())
@app.get_prompt()
async def get_prompt(
name: str, arguments: dict[str, str] | None = None
) -> types.GetPromptResult:
if name not in PROMPTS:
raise ValueError(f"Prompt not found: {name}")
if name == "git-commit":
changes = arguments.get("changes") if arguments else ""
return types.GetPromptResult(
messages=[
types.PromptMessage(
role="user",
content=types.TextContent(
type="text",
text=f"Generate a concise but descriptive commit message "
f"for these changes:\n\n{changes}"
)
)
]
)
if name == "explain-code":
code = arguments.get("code") if arguments else ""
language = arguments.get("language", "Unknown") if arguments else "Unknown"
return types.GetPromptResult(
messages=[
types.PromptMessage(
role="user",
content=types.TextContent(
type="text",
text=f"Explain how this {language} code works:\n\n{code}"
)
)
]
)
raise ValueError("Prompt implementation not found")
```
</Tab>
</Tabs>
## Best practices
When implementing prompts:
1. Use clear, descriptive prompt names
2. Provide detailed descriptions for prompts and arguments
3. Validate all required arguments
4. Handle missing arguments gracefully
5. Consider versioning for prompt templates
6. Cache dynamic content when appropriate
7. Implement error handling
8. Document expected argument formats
9. Consider prompt composability
10. Test prompts with various inputs
## UI integration
Prompts can be surfaced in client UIs as:
* Slash commands
* Quick actions
* Context menu items
* Command palette entries
* Guided workflows
* Interactive forms
## Updates and changes
Servers can notify clients about prompt changes:
1. Server capability: `prompts.listChanged`
2. Notification: `notifications/prompts/list_changed`
3. Client re-fetches prompt list
## Security considerations
When implementing prompts:
* Validate all arguments
* Sanitize user input
* Consider rate limiting
* Implement access controls
* Audit prompt usage
* Handle sensitive data appropriately
* Validate generated content
* Implement timeouts
* Consider prompt injection risks
* Document security requirements
# Resources
Expose data and content from your servers to LLMs
Resources are a core primitive in the Model Context Protocol (MCP) that allow servers to expose data and content that can be read by clients and used as context for LLM interactions.
<Note>
Resources are designed to be **application-controlled**, meaning that the client application can decide how and when they should be used.
Different MCP clients may handle resources differently. For example:
* Claude Desktop currently requires users to explicitly select resources before they can be used
* Other clients might automatically select resources based on heuristics
* Some implementations may even allow the AI model itself to determine which resources to use
Server authors should be prepared to handle any of these interaction patterns when implementing resource support. In order to expose data to models automatically, server authors should use a **model-controlled** primitive such as [Tools](./tools).
</Note>
## Overview
Resources represent any kind of data that an MCP server wants to make available to clients. This can include:
* File contents
* Database records
* API responses
* Live system data
* Screenshots and images
* Log files
* And more
Each resource is identified by a unique URI and can contain either text or binary data.
## Resource URIs
Resources are identified using URIs that follow this format:
```
[protocol]://[host]/[path]
```
For example:
* `file:///home/user/documents/report.pdf`
* `postgres://database/customers/schema`
* `screen://localhost/display1`
The protocol and path structure is defined by the MCP server implementation. Servers can define their own custom URI schemes.
## Resource types
Resources can contain two types of content:
### Text resources
Text resources contain UTF-8 encoded text data. These are suitable for:
* Source code
* Configuration files
* Log files
* JSON/XML data
* Plain text
### Binary resources
Binary resources contain raw binary data encoded in base64. These are suitable for:
* Images
* PDFs
* Audio files
* Video files
* Other non-text formats
## Resource discovery
Clients can discover available resources through two main methods:
### Direct resources
Servers expose a list of concrete resources via the `resources/list` endpoint. Each resource includes:
```typescript
{
uri: string; // Unique identifier for the resource
name: string; // Human-readable name
description?: string; // Optional description
mimeType?: string; // Optional MIME type
}
```
### Resource templates
For dynamic resources, servers can expose [URI templates](https://datatracker.ietf.org/doc/html/rfc6570) that clients can use to construct valid resource URIs:
```typescript
{
uriTemplate: string; // URI template following RFC 6570
name: string; // Human-readable name for this type
description?: string; // Optional description
mimeType?: string; // Optional MIME type for all matching resources
}
```
## Reading resources
To read a resource, clients make a `resources/read` request with the resource URI.
The server responds with a list of resource contents:
```typescript
{
contents: [
{
uri: string; // The URI of the resource
mimeType?: string; // Optional MIME type
// One of:
text?: string; // For text resources
blob?: string; // For binary resources (base64 encoded)
}
]
}
```
<Tip>
Servers may return multiple resources in response to one `resources/read` request. This could be used, for example, to return a list of files inside a directory when the directory is read.
</Tip>
## Resource updates
MCP supports real-time updates for resources through two mechanisms:
### List changes
Servers can notify clients when their list of available resources changes via the `notifications/resources/list_changed` notification.
### Content changes
Clients can subscribe to updates for specific resources:
1. Client sends `resources/subscribe` with resource URI
2. Server sends `notifications/resources/updated` when the resource changes
3. Client can fetch latest content with `resources/read`
4. Client can unsubscribe with `resources/unsubscribe`
## Example implementation
Here's a simple example of implementing resource support in an MCP server:
<Tabs>
<Tab title="TypeScript">
```typescript
const server = new Server({
name: "example-server",
version: "1.0.0"
}, {
capabilities: {
resources: {}
}
});
// List available resources
server.setRequestHandler(ListResourcesRequestSchema, async () => {
return {
resources: [
{
uri: "file:///logs/app.log",
name: "Application Logs",
mimeType: "text/plain"
}
]
};
});
// Read resource contents
server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
const uri = request.params.uri;
if (uri === "file:///logs/app.log") {
const logContents = await readLogFile();
return {
contents: [
{
uri,
mimeType: "text/plain",
text: logContents
}
]
};
}
throw new Error("Resource not found");
});
```
</Tab>
<Tab title="Python">
```python
app = Server("example-server")
@app.list_resources()
async def list_resources() -> list[types.Resource]:
return [
types.Resource(
uri="file:///logs/app.log",
name="Application Logs",
mimeType="text/plain"
)
]
@app.read_resource()
async def read_resource(uri: AnyUrl) -> str:
if str(uri) == "file:///logs/app.log":
log_contents = await read_log_file()
return log_contents
raise ValueError("Resource not found")
# Start server
async with stdio_server() as streams:
await app.run(
streams[0],
streams[1],
app.create_initialization_options()
)
```
</Tab>
</Tabs>
## Best practices
When implementing resource support:
1. Use clear, descriptive resource names and URIs
2. Include helpful descriptions to guide LLM understanding
3. Set appropriate MIME types when known
4. Implement resource templates for dynamic content
5. Use subscriptions for frequently changing resources
6. Handle errors gracefully with clear error messages
7. Consider pagination for large resource lists
8. Cache resource contents when appropriate
9. Validate URIs before processing
10. Document your custom URI schemes
## Security considerations
When exposing resources:
* Validate all resource URIs
* Implement appropriate access controls
* Sanitize file paths to prevent directory traversal
* Be cautious with binary data handling
* Consider rate limiting for resource reads
* Audit resource access
* Encrypt sensitive data in transit
* Validate MIME types
* Implement timeouts for long-running reads
* Handle resource cleanup appropriately
# Sampling
Let your servers request completions from LLMs
Sampling is a powerful MCP feature that allows servers to request LLM completions through the client, enabling sophisticated agentic behaviors while maintaining security and privacy.
<Info>
This feature of MCP is not yet supported in the Claude Desktop client.
</Info>
## How sampling works
The sampling flow follows these steps:
1. Server sends a `sampling/createMessage` request to the client
2. Client reviews the request and can modify it
3. Client samples from an LLM
4. Client reviews the completion
5. Client returns the result to the server
This human-in-the-loop design ensures users maintain control over what the LLM sees and generates.
## Message format
Sampling requests use a standardized message format:
```typescript
{
messages: [
{
role: "user" | "assistant",
content: {
type: "text" | "image",
// For text:
text?: string,
// For images:
data?: string, // base64 encoded
mimeType?: string
}
}
],
modelPreferences?: {
hints?: [{
name?: string // Suggested model name/family
}],
costPriority?: number, // 0-1, importance of minimizing cost
speedPriority?: number, // 0-1, importance of low latency
intelligencePriority?: number // 0-1, importance of capabilities
},
systemPrompt?: string,
includeContext?: "none" | "thisServer" | "allServers",
temperature?: number,
maxTokens: number,
stopSequences?: string[],
metadata?: Record<string, unknown>
}
```
## Request parameters
### Messages
The `messages` array contains the conversation history to send to the LLM. Each message has:
* `role`: Either "user" or "assistant"
* `content`: The message content, which can be:
* Text content with a `text` field
* Image content with `data` (base64) and `mimeType` fields
### Model preferences
The `modelPreferences` object allows servers to specify their model selection preferences:
* `hints`: Array of model name suggestions that clients can use to select an appropriate model:
* `name`: String that can match full or partial model names (e.g. "claude-3", "sonnet")
* Clients may map hints to equivalent models from different providers
* Multiple hints are evaluated in preference order
* Priority values (0-1 normalized):
* `costPriority`: Importance of minimizing costs
* `speedPriority`: Importance of low latency response
* `intelligencePriority`: Importance of advanced model capabilities
Clients make the final model selection based on these preferences and their available models.
### System prompt
An optional `systemPrompt` field allows servers to request a specific system prompt. The client may modify or ignore this.
### Context inclusion
The `includeContext` parameter specifies what MCP context to include:
* `"none"`: No additional context
* `"thisServer"`: Include context from the requesting server
* `"allServers"`: Include context from all connected MCP servers
The client controls what context is actually included.
### Sampling parameters
Fine-tune the LLM sampling with:
* `temperature`: Controls randomness (0.0 to 1.0)
* `maxTokens`: Maximum tokens to generate
* `stopSequences`: Array of sequences that stop generation
* `metadata`: Additional provider-specific parameters
## Response format
The client returns a completion result:
```typescript
{
model: string, // Name of the model used
stopReason?: "endTurn" | "stopSequence" | "maxTokens" | string,
role: "user" | "assistant",
content: {
type: "text" | "image",
text?: string,
data?: string,
mimeType?: string
}
}
```
## Example request
Here's an example of requesting sampling from a client:
```json
{
"method": "sampling/createMessage",
"params": {
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": "What files are in the current directory?"
}
}
],
"systemPrompt": "You are a helpful file system assistant.",
"includeContext": "thisServer",
"maxTokens": 100
}
}
```
## Best practices
When implementing sampling:
1. Always provide clear, well-structured prompts
2. Handle both text and image content appropriately
3. Set reasonable token limits
4. Include relevant context through `includeContext`
5. Validate responses before using them
6. Handle errors gracefully
7. Consider rate limiting sampling requests
8. Document expected sampling behavior
9. Test with various model parameters
10. Monitor sampling costs
## Human in the loop controls
Sampling is designed with human oversight in mind:
### For prompts
* Clients should show users the proposed prompt
* Users should be able to modify or reject prompts
* System prompts can be filtered or modified
* Context inclusion is controlled by the client
### For completions
* Clients should show users the completion
* Users should be able to modify or reject completions
* Clients can filter or modify completions
* Users control which model is used
## Security considerations
When implementing sampling:
* Validate all message content
* Sanitize sensitive information
* Implement appropriate rate limits
* Monitor sampling usage
* Encrypt data in transit
* Handle user data privacy
* Audit sampling requests
* Control cost exposure
* Implement timeouts
* Handle model errors gracefully
## Common patterns
### Agentic workflows
Sampling enables agentic patterns like:
* Reading and analyzing resources
* Making decisions based on context
* Generating structured data
* Handling multi-step tasks
* Providing interactive assistance
### Context management
Best practices for context:
* Request minimal necessary context
* Structure context clearly
* Handle context size limits
* Update context as needed
* Clean up stale context
### Error handling
Robust error handling should:
* Catch sampling failures
* Handle timeout errors
* Manage rate limits
* Validate responses
* Provide fallback behaviors
* Log errors appropriately
## Limitations
Be aware of these limitations:
* Sampling depends on client capabilities
* Users control sampling behavior
* Context size has limits
* Rate limits may apply
* Costs should be considered
* Model availability varies
* Response times vary
* Not all content types supported
# Tools
Enable LLMs to perform actions through your server
Tools are a powerful primitive in the Model Context Protocol (MCP) that enable servers to expose executable functionality to clients. Through tools, LLMs can interact with external systems, perform computations, and take actions in the real world.
<Note>
Tools are designed to be **model-controlled**, meaning that tools are exposed from servers to clients with the intention of the AI model being able to automatically invoke them (with a human in the loop to grant approval).
</Note>
## Overview
Tools in MCP allow servers to expose executable functions that can be invoked by clients and used by LLMs to perform actions. Key aspects of tools include:
* **Discovery**: Clients can list available tools through the `tools/list` endpoint
* **Invocation**: Tools are called using the `tools/call` endpoint, where servers perform the requested operation and return results
* **Flexibility**: Tools can range from simple calculations to complex API interactions
Like [resources](/docs/concepts/resources), tools are identified by unique names and can include descriptions to guide their usage. However, unlike resources, tools represent dynamic operations that can modify state or interact with external systems.
## Tool definition structure
Each tool is defined with the following structure:
```typescript
{
name: string; // Unique identifier for the tool
description?: string; // Human-readable description
inputSchema: { // JSON Schema for the tool's parameters
type: "object",
properties: { ... } // Tool-specific parameters
}
}
```
## Implementing tools
Here's an example of implementing a basic tool in an MCP server:
<Tabs>
<Tab title="TypeScript">
```typescript
const server = new Server({
name: "example-server",
version: "1.0.0"
}, {
capabilities: {
tools: {}
}
});
// Define available tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [{
name: "calculate_sum",
description: "Add two numbers together",
inputSchema: {
type: "object",
properties: {
a: { type: "number" },
b: { type: "number" }
},
required: ["a", "b"]
}
}]
};
});
// Handle tool execution
server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === "calculate_sum") {
const { a, b } = request.params.arguments;
return {
toolResult: a + b
};
}
throw new Error("Tool not found");
});
```
</Tab>
<Tab title="Python">
```python
app = Server("example-server")
@app.list_tools()
async def list_tools() -> list[types.Tool]:
return [
types.Tool(
name="calculate_sum",
description="Add two numbers together",
inputSchema={
"type": "object",
"properties": {
"a": {"type": "number"},
"b": {"type": "number"}
},
"required": ["a", "b"]
}
)
]
@app.call_tool()
async def call_tool(
name: str,
arguments: dict
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
if name == "calculate_sum":
a = arguments["a"]
b = arguments["b"]
result = a + b
return [types.TextContent(type="text", text=str(result))]
raise ValueError(f"Tool not found: {name}")
```
</Tab>
</Tabs>
## Example tool patterns
Here are some examples of types of tools that a server could provide:
### System operations
Tools that interact with the local system:
```typescript
{
name: "execute_command",
description: "Run a shell command",
inputSchema: {
type: "object",
properties: {
command: { type: "string" },
args: { type: "array", items: { type: "string" } }
}
}
}
```
### API integrations
Tools that wrap external APIs:
```typescript
{
name: "github_create_issue",
description: "Create a GitHub issue",
inputSchema: {
type: "object",
properties: {
title: { type: "string" },
body: { type: "string" },
labels: { type: "array", items: { type: "string" } }
}
}
}
```
### Data processing
Tools that transform or analyze data:
```typescript
{
name: "analyze_csv",
description: "Analyze a CSV file",
inputSchema: {
type: "object",
properties: {
filepath: { type: "string" },
operations: {
type: "array",
items: {
enum: ["sum", "average", "count"]
}
}
}
}
}
```
## Best practices
When implementing tools:
1. Provide clear, descriptive names and descriptions
2. Use detailed JSON Schema definitions for parameters
3. Include examples in tool descriptions to demonstrate how the model should use them
4. Implement proper error handling and validation
5. Use progress reporting for long operations
6. Keep tool operations focused and atomic
7. Document expected return value structures
8. Implement proper timeouts
9. Consider rate limiting for resource-intensive operations
10. Log tool usage for debugging and monitoring
## Security considerations
When exposing tools:
### Input validation
* Validate all parameters against the schema
* Sanitize file paths and system commands
* Validate URLs and external identifiers
* Check parameter sizes and ranges
* Prevent command injection
### Access control
* Implement authentication where needed
* Use appropriate authorization checks
* Audit tool usage
* Rate limit requests
* Monitor for abuse
### Error handling
* Don't expose internal errors to clients
* Log security-relevant errors
* Handle timeouts appropriately
* Clean up resources after errors
* Validate return values
## Tool discovery and updates
MCP supports dynamic tool discovery:
1. Clients can list available tools at any time
2. Servers can notify clients when tools change using `notifications/tools/list_changed`
3. Tools can be added or removed during runtime
4. Tool definitions can be updated (though this should be done carefully)
## Error handling
Tool errors should be reported within the result object, not as MCP protocol-level errors. This allows the LLM to see and potentially handle the error. When a tool encounters an error:
1. Set `isError` to `true` in the result
2. Include error details in the `content` array
Here's an example of proper error handling for tools:
<Tabs>
<Tab title="TypeScript">
```typescript
try {
// Tool operation
const result = performOperation();
return {
content: [
{
type: "text",
text: `Operation successful: ${result}`
}
]
};
} catch (error) {
return {
isError: true,
content: [
{
type: "text",
text: `Error: ${error.message}`
}
]
};
}
```
</Tab>
<Tab title="Python">
```python
try:
# Tool operation
result = perform_operation()
return types.CallToolResult(
content=[
types.TextContent(
type="text",
text=f"Operation successful: {result}"
)
]
)
except Exception as error:
return types.CallToolResult(
isError=True,
content=[
types.TextContent(
type="text",
text=f"Error: {str(error)}"
)
]
)
```
</Tab>
</Tabs>
This approach allows the LLM to see that an error occurred and potentially take corrective action or request human intervention.
## Testing tools
A comprehensive testing strategy for MCP tools should cover:
* **Functional testing**: Verify tools execute correctly with valid inputs and handle invalid inputs appropriately
* **Integration testing**: Test tool interaction with external systems using both real and mocked dependencies
* **Security testing**: Validate authentication, authorization, input sanitization, and rate limiting
* **Performance testing**: Check behavior under load, timeout handling, and resource cleanup
* **Error handling**: Ensure tools properly report errors through the MCP protocol and clean up resources
# Transports
Learn about MCP's communication mechanisms
Transports in the Model Context Protocol (MCP) provide the foundation for communication between clients and servers. A transport handles the underlying mechanics of how messages are sent and received.
## Message Format
MCP uses [JSON-RPC](https://www.jsonrpc.org/) 2.0 as its wire format. The transport layer is responsible for converting MCP protocol messages into JSON-RPC format for transmission and converting received JSON-RPC messages back into MCP protocol messages.
There are three types of JSON-RPC messages used:
### Requests
```typescript
{
jsonrpc: "2.0",
id: number | string,
method: string,
params?: object
}
```
### Responses
```typescript
{
jsonrpc: "2.0",
id: number | string,
result?: object,
error?: {
code: number,
message: string,
data?: unknown
}
}
```
### Notifications
```typescript
{
jsonrpc: "2.0",
method: string,
params?: object
}
```
## Built-in Transport Types
MCP includes two standard transport implementations:
### Standard Input/Output (stdio)
The stdio transport enables communication through standard input and output streams. This is particularly useful for local integrations and command-line tools.
Use stdio when:
* Building command-line tools
* Implementing local integrations
* Needing simple process communication
* Working with shell scripts
<Tabs>
<Tab title="TypeScript (Server)">
```typescript
const server = new Server({
name: "example-server",
version: "1.0.0"
}, {
capabilities: {}
});
const transport = new StdioServerTransport();
await server.connect(transport);
```
</Tab>
<Tab title="TypeScript (Client)">
```typescript
const client = new Client({
name: "example-client",
version: "1.0.0"
}, {
capabilities: {}
});
const transport = new StdioClientTransport({
command: "./server",
args: ["--option", "value"]
});
await client.connect(transport);
```
</Tab>
<Tab title="Python (Server)">
```python
app = Server("example-server")
async with stdio_server() as streams:
await app.run(
streams[0],
streams[1],
app.create_initialization_options()
)
```
</Tab>
<Tab title="Python (Client)">
```python
params = StdioServerParameters(
command="./server",
args=["--option", "value"]
)
async with stdio_client(params) as streams:
async with ClientSession(streams[0], streams[1]) as session:
await session.initialize()
```
</Tab>
</Tabs>
### Server-Sent Events (SSE)
SSE transport enables server-to-client streaming with HTTP POST requests for client-to-server communication.
Use SSE when:
* Only server-to-client streaming is needed
* Working with restricted networks
* Implementing simple updates
<Tabs>
<Tab title="TypeScript (Server)">
```typescript
const server = new Server({
name: "example-server",
version: "1.0.0"
}, {
capabilities: {}
});
const transport = new SSEServerTransport("/message", response);
await server.connect(transport);
```
</Tab>
<Tab title="TypeScript (Client)">
```typescript
const client = new Client({
name: "example-client",
version: "1.0.0"
}, {
capabilities: {}
});
const transport = new SSEClientTransport(
new URL("http://localhost:3000/sse")
);
await client.connect(transport);
```
</Tab>
<Tab title="Python (Server)">
```python
from mcp.server.sse import SseServerTransport
from starlette.applications import Starlette
from starlette.routing import Route
app = Server("example-server")
sse = SseServerTransport("/messages")
async def handle_sse(scope, receive, send):
async with sse.connect_sse(scope, receive, send) as streams:
await app.run(streams[0], streams[1], app.create_initialization_options())
async def handle_messages(scope, receive, send):
await sse.handle_post_message(scope, receive, send)
starlette_app = Starlette(
routes=[
Route("/sse", endpoint=handle_sse),
Route("/messages", endpoint=handle_messages, methods=["POST"]),
]
)
```
</Tab>
<Tab title="Python (Client)">
```python
async with sse_client("http://localhost:8000/sse") as streams:
async with ClientSession(streams[0], streams[1]) as session:
await session.initialize()
```
</Tab>
</Tabs>
## Custom Transports
MCP makes it easy to implement custom transports for specific needs. Any transport implementation just needs to conform to the Transport interface:
You can implement custom transports for:
* Custom network protocols
* Specialized communication channels
* Integration with existing systems
* Performance optimization
<Tabs>
<Tab title="TypeScript">
```typescript
interface Transport {
// Start processing messages
start(): Promise<void>;
// Send a JSON-RPC message
send(message: JSONRPCMessage): Promise<void>;
// Close the connection
close(): Promise<void>;
// Callbacks
onclose?: () => void;
onerror?: (error: Error) => void;
onmessage?: (message: JSONRPCMessage) => void;
}
```
</Tab>
<Tab title="Python">
Note that while MCP Servers are often implemented with asyncio, we recommend
implementing low-level interfaces like transports with `anyio` for wider compatibility.
```python
@contextmanager
async def create_transport(
read_stream: MemoryObjectReceiveStream[JSONRPCMessage | Exception],
write_stream: MemoryObjectSendStream[JSONRPCMessage]
):
"""
Transport interface for MCP.
Args:
read_stream: Stream to read incoming messages from
write_stream: Stream to write outgoing messages to
"""
async with anyio.create_task_group() as tg:
try:
# Start processing messages
tg.start_soon(lambda: process_messages(read_stream))
# Send messages
async with write_stream:
yield write_stream
except Exception as exc:
# Handle errors
raise exc
finally:
# Clean up
tg.cancel_scope.cancel()
await write_stream.aclose()
await read_stream.aclose()
```
</Tab>
</Tabs>
## Error Handling
Transport implementations should handle various error scenarios:
1. Connection errors
2. Message parsing errors
3. Protocol errors
4. Network timeouts
5. Resource cleanup
Example error handling:
<Tabs>
<Tab title="TypeScript">
```typescript
class ExampleTransport implements Transport {
async start() {
try {
// Connection logic
} catch (error) {
this.onerror?.(new Error(`Failed to connect: ${error}`));
throw error;
}
}
async send(message: JSONRPCMessage) {
try {
// Sending logic
} catch (error) {
this.onerror?.(new Error(`Failed to send message: ${error}`));
throw error;
}
}
}
```
</Tab>
<Tab title="Python">
Note that while MCP Servers are often implemented with asyncio, we recommend
implementing low-level interfaces like transports with `anyio` for wider compatibility.
```python
@contextmanager
async def example_transport(scope: Scope, receive: Receive, send: Send):
try:
# Create streams for bidirectional communication
read_stream_writer, read_stream = anyio.create_memory_object_stream(0)
write_stream, write_stream_reader = anyio.create_memory_object_stream(0)
async def message_handler():
try:
async with read_stream_writer:
# Message handling logic
pass
except Exception as exc:
logger.error(f"Failed to handle message: {exc}")
raise exc
async with anyio.create_task_group() as tg:
tg.start_soon(message_handler)
try:
# Yield streams for communication
yield read_stream, write_stream
except Exception as exc:
logger.error(f"Transport error: {exc}")
raise exc
finally:
tg.cancel_scope.cancel()
await write_stream.aclose()
await read_stream.aclose()
except Exception as exc:
logger.error(f"Failed to initialize transport: {exc}")
raise exc
```
</Tab>
</Tabs>
## Best Practices
When implementing or using MCP transport:
1. Handle connection lifecycle properly
2. Implement proper error handling
3. Clean up resources on connection close
4. Use appropriate timeouts
5. Validate messages before sending
6. Log transport events for debugging
7. Implement reconnection logic when appropriate
8. Handle backpressure in message queues
9. Monitor connection health
10. Implement proper security measures
## Security Considerations
When implementing transport:
### Authentication and Authorization
* Implement proper authentication mechanisms
* Validate client credentials
* Use secure token handling
* Implement authorization checks
### Data Security
* Use TLS for network transport
* Encrypt sensitive data
* Validate message integrity
* Implement message size limits
* Sanitize input data
### Network Security
* Implement rate limiting
* Use appropriate timeouts
* Handle denial of service scenarios
* Monitor for unusual patterns
* Implement proper firewall rules
## Debugging Transport
Tips for debugging transport issues:
1. Enable debug logging
2. Monitor message flow
3. Check connection states
4. Validate message formats
5. Test error scenarios
6. Use network analysis tools
7. Implement health checks
8. Monitor resource usage
9. Test edge cases
10. Use proper error tracking
# Python
Create a simple MCP server in Python in 15 minutes
Let's build your first MCP server in Python! We'll create a weather server that provides current weather data as a resource and lets Claude fetch forecasts using tools.
<Note>
This guide uses the OpenWeatherMap API. You'll need a free API key from [OpenWeatherMap](https://openweathermap.org/api) to follow along.
</Note>
## Prerequisites
<Info>
The following steps are for macOS. Guides for other platforms are coming soon.
</Info>
<Steps>
<Step title="Install Python">
You'll need Python 3.10 or higher:
```bash
python --version # Should be 3.10 or higher
```
</Step>
<Step title="Install uv via homebrew">
See [https://docs.astral.sh/uv/](https://docs.astral.sh/uv/) for more information.
```bash
brew install uv
uv --version # Should be 0.4.18 or higher
```
</Step>
<Step title="Create a new project using the MCP project creator">
```bash
uvx create-mcp-server --path weather_service
cd weather_service
```
</Step>
<Step title="Install additional dependencies">
```bash
uv add httpx python-dotenv
```
</Step>
<Step title="Set up environment">
Create `.env`:
```bash
OPENWEATHER_API_KEY=your-api-key-here
```
</Step>
</Steps>
## Create your server
<Steps>
<Step title="Add the base imports and setup">
In `weather_service/src/weather_service/server.py`
```python
import os
import json
import logging
from datetime import datetime, timedelta
from collections.abc import Sequence
from functools import lru_cache
from typing import Any
import httpx
import asyncio
from dotenv import load_dotenv
from mcp.server import Server
from mcp.types import (
Resource,
Tool,
TextContent,
ImageContent,
EmbeddedResource,
LoggingLevel
)
from pydantic import AnyUrl
# Load environment variables
load_dotenv()
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("weather-server")
# API configuration
API_KEY = os.getenv("OPENWEATHER_API_KEY")
if not API_KEY:
raise ValueError("OPENWEATHER_API_KEY environment variable required")
API_BASE_URL = "http://api.openweathermap.org/data/2.5"
DEFAULT_CITY = "London"
CURRENT_WEATHER_ENDPOINT = "weather"
FORECAST_ENDPOINT = "forecast"
# The rest of our server implementation will go here
```
</Step>
<Step title="Add weather fetching functionality">
Add this functionality:
```python
# Create reusable params
http_params = {
"appid": API_KEY,
"units": "metric"
}
async def fetch_weather(city: str) -> dict[str, Any]:
async with httpx.AsyncClient() as client:
response = await client.get(
f"{API_BASE_URL}/weather",
params={"q": city, **http_params}
)
response.raise_for_status()
data = response.json()
return {
"temperature": data["main"]["temp"],
"conditions": data["weather"][0]["description"],
"humidity": data["main"]["humidity"],
"wind_speed": data["wind"]["speed"],
"timestamp": datetime.now().isoformat()
}
app = Server("weather-server")
```
</Step>
<Step title="Implement resource handlers">
Add these resource-related handlers to our main function:
```python
app = Server("weather-server")
@app.list_resources()
async def list_resources() -> list[Resource]:
"""List available weather resources."""
uri = AnyUrl(f"weather://{DEFAULT_CITY}/current")
return [
Resource(
uri=uri,
name=f"Current weather in {DEFAULT_CITY}",
mimeType="application/json",
description="Real-time weather data"
)
]
@app.read_resource()
async def read_resource(uri: AnyUrl) -> str:
"""Read current weather data for a city."""
city = DEFAULT_CITY
if str(uri).startswith("weather://") and str(uri).endswith("/current"):
city = str(uri).split("/")[-2]
else:
raise ValueError(f"Unknown resource: {uri}")
try:
weather_data = await fetch_weather(city)
return json.dumps(weather_data, indent=2)
except httpx.HTTPError as e:
raise RuntimeError(f"Weather API error: {str(e)}")
```
</Step>
<Step title="Implement tool handlers">
Add these tool-related handlers:
```python
app = Server("weather-server")
# Resource implementation ...
@app.list_tools()
async def list_tools() -> list[Tool]:
"""List available weather tools."""
return [
Tool(
name="get_forecast",
description="Get weather forecast for a city",
inputSchema={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name"
},
"days": {
"type": "number",
"description": "Number of days (1-5)",
"minimum": 1,
"maximum": 5
}
},
"required": ["city"]
}
)
]
@app.call_tool()
async def call_tool(name: str, arguments: Any) -> Sequence[TextContent | ImageContent | EmbeddedResource]:
"""Handle tool calls for weather forecasts."""
if name != "get_forecast":
raise ValueError(f"Unknown tool: {name}")
if not isinstance(arguments, dict) or "city" not in arguments:
raise ValueError("Invalid forecast arguments")
city = arguments["city"]
days = min(int(arguments.get("days", 3)), 5)
try:
async with httpx.AsyncClient() as client:
response = await client.get(
f"{API_BASE_URL}/{FORECAST_ENDPOINT}",
params={
"q": city,
"cnt": days * 8, # API returns 3-hour intervals
**http_params,
}
)
response.raise_for_status()
data = response.json()
forecasts = []
for i in range(0, len(data["list"]), 8):
day_data = data["list"][i]
forecasts.append({
"date": day_data["dt_txt"].split()[0],
"temperature": day_data["main"]["temp"],
"conditions": day_data["weather"][0]["description"]
})
return [
TextContent(
type="text",
text=json.dumps(forecasts, indent=2)
)
]
except httpx.HTTPError as e:
logger.error(f"Weather API error: {str(e)}")
raise RuntimeError(f"Weather API error: {str(e)}")
```
</Step>
<Step title="Add the main function">
Add this to the end of `weather_service/src/weather_service/server.py`:
```python
async def main():
# Import here to avoid issues with event loops
from mcp.server.stdio import stdio_server
async with stdio_server() as (read_stream, write_stream):
await app.run(
read_stream,
write_stream,
app.create_initialization_options()
)
```
</Step>
<Step title="Check your entry point in __init__.py">
Add this to the end of `weather_service/src/weather_service/__init__.py`:
```python
from . import server
import asyncio
def main():
"""Main entry point for the package."""
asyncio.run(server.main())
# Optionally expose other important items at package level
__all__ = ['main', 'server']
```
</Step>
</Steps>
## Connect to Claude Desktop
<Steps>
<Step title="Update Claude config">
Add to `claude_desktop_config.json`:
```json
{
"mcpServers": {
"weather": {
"command": "uv",
"args": [
"--directory",
"path/to/your/project",
"run",
"weather-service"
],
"env": {
"OPENWEATHER_API_KEY": "your-api-key"
}
}
}
}
```
</Step>
<Step title="Restart Claude">
1. Quit Claude completely
2. Start Claude again
3. Look for your weather server in the 🔌 menu
</Step>
</Steps>
## Try it out!
<AccordionGroup>
<Accordion title="Check Current Weather" active>
Ask Claude:
```
What's the current weather in San Francisco? Can you analyze the conditions and tell me if it's a good day for outdoor activities?
```
</Accordion>
<Accordion title="Get a Forecast">
Ask Claude:
```
Can you get me a 5-day forecast for Tokyo and help me plan what clothes to pack for my trip?
```
</Accordion>
<Accordion title="Compare Weather">
Ask Claude:
```
Can you analyze the forecast for both Tokyo and San Francisco and tell me which city would be better for outdoor photography this week?
```
</Accordion>
</AccordionGroup>
## Understanding the code
<Tabs>
<Tab title="Type Hints">
```python
async def read_resource(self, uri: str) -> ReadResourceResult:
# ...
```
Python type hints help catch errors early and improve code maintainability.
</Tab>
<Tab title="Resources">
```python
@app.list_resources()
async def list_resources(self) -> ListResourcesResult:
return ListResourcesResult(
resources=[
Resource(
uri=f"weather://{DEFAULT_CITY}/current",
name=f"Current weather in {DEFAULT_CITY}",
mimeType="application/json",
description="Real-time weather data"
)
]
)
```
Resources provide data that Claude can access as context.
</Tab>
<Tab title="Tools">
```python
Tool(
name="get_forecast",
description="Get weather forecast for a city",
inputSchema={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name"
},
"days": {
"type": "number",
"description": "Number of days (1-5)",
"minimum": 1,
"maximum": 5
}
},
"required": ["city"]
}
)
```
Tools let Claude take actions through your server with validated inputs.
</Tab>
<Tab title="Server Structure">
```python
# Create server instance with name
app = Server("weather-server")
# Register resource handler
@app.list_resources()
async def list_resources() -> list[Resource]:
"""List available resources"""
return [...]
# Register tool handler
@app.call_tool()
async def call_tool(name: str, arguments: Any) -> Sequence[TextContent]:
"""Handle tool execution"""
return [...]
# Register additional handlers
@app.read_resource()
...
@app.list_tools()
...
```
The MCP server uses a simple app pattern - create a Server instance and register handlers with decorators. Each handler maps to a specific MCP protocol operation.
</Tab>
</Tabs>
## Best practices
<CardGroup cols={1}>
<Card title="Error Handling" icon="shield">
```python
try:
async with httpx.AsyncClient() as client:
response = await client.get(..., params={..., **http_params})
response.raise_for_status()
except httpx.HTTPError as e:
raise McpError(
ErrorCode.INTERNAL_ERROR,
f"API error: {str(e)}"
)
```
</Card>
<Card title="Type Validation" icon="check">
```python
if not isinstance(args, dict) or "city" not in args:
raise McpError(
ErrorCode.INVALID_PARAMS,
"Invalid forecast arguments"
)
```
</Card>
<Card title="Environment Variables" icon="gear">
```python
if not API_KEY:
raise ValueError("OPENWEATHER_API_KEY is required")
```
</Card>
</CardGroup>
## Available transports
While this guide uses stdio transport, MCP supports additional transport options:
### SSE (Server-Sent Events)
```python
from mcp.server.sse import SseServerTransport
from starlette.applications import Starlette
from starlette.routing import Route
# Create SSE transport with endpoint
sse = SseServerTransport("/messages")
# Handler for SSE connections
async def handle_sse(scope, receive, send):
async with sse.connect_sse(scope, receive, send) as streams:
await app.run(
streams[0], streams[1], app.create_initialization_options()
)
# Handler for client messages
async def handle_messages(scope, receive, send):
await sse.handle_post_message(scope, receive, send)
# Create Starlette app with routes
app = Starlette(
debug=True,
routes=[
Route("/sse", endpoint=handle_sse),
Route("/messages", endpoint=handle_messages, methods=["POST"]),
],
)
# Run with any ASGI server
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
```
## Advanced features
<Steps>
<Step title="Understanding Request Context">
The request context provides access to the current request's metadata and the active client session. Access it through `server.request_context`:
```python
@app.call_tool()
async def call_tool(name: str, arguments: Any) -> Sequence[TextContent]:
# Access the current request context
ctx = self.request_context
# Get request metadata like progress tokens
if progress_token := ctx.meta.progressToken:
# Send progress notifications via the session
await ctx.session.send_progress_notification(
progress_token=progress_token,
progress=0.5,
total=1.0
)
# Sample from the LLM client
result = await ctx.session.create_message(
messages=[
SamplingMessage(
role="user",
content=TextContent(
type="text",
text="Analyze this weather data: " + json.dumps(arguments)
)
)
],
max_tokens=100
)
return [TextContent(type="text", text=result.content.text)]
```
</Step>
<Step title="Add caching">
```python
# Cache settings
cache_timeout = timedelta(minutes=15)
last_cache_time = None
cached_weather = None
async def fetch_weather(city: str) -> dict[str, Any]:
global cached_weather, last_cache_time
now = datetime.now()
if (cached_weather is None or
last_cache_time is None or
now - last_cache_time > cache_timeout):
async with httpx.AsyncClient() as client:
response = await client.get(
f"{API_BASE_URL}/{CURRENT_WEATHER_ENDPOINT}",
params={"q": city, **http_params}
)
response.raise_for_status()
data = response.json()
cached_weather = {
"temperature": data["main"]["temp"],
"conditions": data["weather"][0]["description"],
"humidity": data["main"]["humidity"],
"wind_speed": data["wind"]["speed"],
"timestamp": datetime.now().isoformat()
}
last_cache_time = now
return cached_weather
```
</Step>
<Step title="Add progress notifications">
```python
@self.call_tool()
async def call_tool(self, name: str, arguments: Any) -> CallToolResult:
if progress_token := self.request_context.meta.progressToken:
# Send progress notifications
await self.request_context.session.send_progress_notification(
progress_token=progress_token,
progress=1,
total=2
)
# Fetch data...
await self.request_context.session.send_progress_notification(
progress_token=progress_token,
progress=2,
total=2
)
# Rest of the method implementation...
```
</Step>
<Step title="Add logging support">
```python
# Set up logging
logger = logging.getLogger("weather-server")
logger.setLevel(logging.INFO)
@app.set_logging_level()
async def set_logging_level(level: LoggingLevel) -> EmptyResult:
logger.setLevel(level.upper())
await app.request_context.session.send_log_message(
level="info",
data=f"Log level set to {level}",
logger="weather-server"
)
return EmptyResult()
# Use logger throughout the code
# For example:
# logger.info("Weather data fetched successfully")
# logger.error(f"Error fetching weather data: {str(e)}")
```
</Step>
<Step title="Add resource templates">
```python
@app.list_resource_templates()
async def list_resource_templates() -> list[ResourceTemplate]:
return [
ResourceTemplate(
uriTemplate="weather://{city}/current",
name="Current weather for any city",
mimeType="application/json"
)
]
```
</Step>
</Steps>
## Testing
<Steps>
<Step title="Create test file">
Create `tests/weather_test.py`:
```python
import pytest
import os
from unittest.mock import patch, Mock
from datetime import datetime
import json
from pydantic import AnyUrl
os.environ["OPENWEATHER_API_KEY"] = "TEST"
from weather_service.server import (
fetch_weather,
read_resource,
call_tool,
list_resources,
list_tools,
DEFAULT_CITY
)
@pytest.fixture
def anyio_backend():
return "asyncio"
@pytest.fixture
def mock_weather_response():
return {
"main": {
"temp": 20.5,
"humidity": 65
},
"weather": [
{"description": "scattered clouds"}
],
"wind": {
"speed": 3.6
}
}
@pytest.fixture
def mock_forecast_response():
return {
"list": [
{
"dt_txt": "2024-01-01 12:00:00",
"main": {"temp": 18.5},
"weather": [{"description": "sunny"}]
},
{
"dt_txt": "2024-01-02 12:00:00",
"main": {"temp": 17.2},
"weather": [{"description": "cloudy"}]
}
]
}
@pytest.mark.anyio
async def test_fetch_weather(mock_weather_response):
with patch('requests.Session.get') as mock_get:
mock_get.return_value.json.return_value = mock_weather_response
mock_get.return_value.raise_for_status = Mock()
weather = await fetch_weather("London")
assert weather["temperature"] == 20.5
assert weather["conditions"] == "scattered clouds"
assert weather["humidity"] == 65
assert weather["wind_speed"] == 3.6
assert "timestamp" in weather
@pytest.mark.anyio
async def test_read_resource():
with patch('weather_service.server.fetch_weather') as mock_fetch:
mock_fetch.return_value = {
"temperature": 20.5,
"conditions": "clear sky",
"timestamp": datetime.now().isoformat()
}
uri = AnyUrl("weather://London/current")
result = await read_resource(uri)
assert isinstance(result, str)
assert "temperature" in result
assert "clear sky" in result
@pytest.mark.anyio
async def test_call_tool(mock_forecast_response):
class Response():
def raise_for_status(self):
pass
def json(self):
return mock_forecast_response
class AsyncClient():
async def __aenter__(self):
return self
async def __aexit__(self, *exc_info):
pass
async def get(self, *args, **kwargs):
return Response()
with patch('httpx.AsyncClient', new=AsyncClient) as mock_client:
result = await call_tool("get_forecast", {"city": "London", "days": 2})
assert len(result) == 1
assert result[0].type == "text"
forecast_data = json.loads(result[0].text)
assert len(forecast_data) == 1
assert forecast_data[0]["temperature"] == 18.5
assert forecast_data[0]["conditions"] == "sunny"
@pytest.mark.anyio
async def test_list_resources():
resources = await list_resources()
assert len(resources) == 1
assert resources[0].name == f"Current weather in {DEFAULT_CITY}"
assert resources[0].mimeType == "application/json"
@pytest.mark.anyio
async def test_list_tools():
tools = await list_tools()
assert len(tools) == 1
assert tools[0].name == "get_forecast"
assert "city" in tools[0].inputSchema["properties"]
```
</Step>
<Step title="Run tests">
```bash
uv add --dev pytest
uv run pytest
```
</Step>
</Steps>
## Troubleshooting
### Installation issues
```bash
# Check Python version
python --version
# Reinstall dependencies
uv sync --reinstall
```
### Type checking
```bash
# Install mypy
uv add --dev pyright
# Run type checker
uv run pyright src
```
## Next steps
<CardGroup cols={2}>
<Card title="Architecture overview" icon="sitemap" href="/docs/concepts/architecture">
Learn more about the MCP architecture
</Card>
<Card title="Python SDK" icon="python" href="https://github.com/modelcontextprotocol/python-sdk">
Check out the Python SDK on GitHub
</Card>
</CardGroup>
# TypeScript
Create a simple MCP server in TypeScript in 15 minutes
Let's build your first MCP server in TypeScript! We'll create a weather server that provides current weather data as a resource and lets Claude fetch forecasts using tools.
<Note>
This guide uses the OpenWeatherMap API. You'll need a free API key from [OpenWeatherMap](https://openweathermap.org/api) to follow along.
</Note>
## Prerequisites
<Steps>
<Step title="Install Node.js">
You'll need Node.js 18 or higher:
```bash
node --version # Should be v18 or higher
npm --version
```
</Step>
<Step title="Create a new project">
You can use our [create-typescript-server](https://github.com/modelcontextprotocol/create-typescript-server) tool to bootstrap a new project:
```bash
npx @modelcontextprotocol/create-server weather-server
cd weather-server
```
</Step>
<Step title="Install dependencies">
```bash
npm install --save axios dotenv
```
</Step>
<Step title="Set up environment">
Create `.env`:
```bash
OPENWEATHER_API_KEY=your-api-key-here
```
Make sure to add your environment file to `.gitignore`
```bash
.env
```
</Step>
</Steps>
## Create your server
<Steps>
<Step title="Define types">
Create a file `src/types.ts`, and add the following:
```typescript
export interface OpenWeatherResponse {
main: {
temp: number;
humidity: number;
};
weather: Array<{
description: string;
}>;
wind: {
speed: number;
};
dt_txt?: string;
}
export interface WeatherData {
temperature: number;
conditions: string;
humidity: number;
wind_speed: number;
timestamp: string;
}
export interface ForecastDay {
date: string;
temperature: number;
conditions: string;
}
export interface GetForecastArgs {
city: string;
days?: number;
}
// Type guard for forecast arguments
export function isValidForecastArgs(args: any): args is GetForecastArgs {
return (
typeof args === "object" &&
args !== null &&
"city" in args &&
typeof args.city === "string" &&
(args.days === undefined || typeof args.days === "number")
);
}
```
</Step>
<Step title="Add the base code">
Replace `src/index.ts` with the following:
```typescript
#!/usr/bin/env node
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
ListResourcesRequestSchema,
ReadResourceRequestSchema,
ListToolsRequestSchema,
CallToolRequestSchema,
ErrorCode,
McpError
} from "@modelcontextprotocol/sdk/types.js";
import axios from "axios";
import dotenv from "dotenv";
import {
WeatherData,
ForecastDay,
OpenWeatherResponse,
isValidForecastArgs
} from "./types.js";
dotenv.config();
const API_KEY = process.env.OPENWEATHER_API_KEY;
if (!API_KEY) {
throw new Error("OPENWEATHER_API_KEY environment variable is required");
}
const API_CONFIG = {
BASE_URL: 'http://api.openweathermap.org/data/2.5',
DEFAULT_CITY: 'San Francisco',
ENDPOINTS: {
CURRENT: 'weather',
FORECAST: 'forecast'
}
} as const;
class WeatherServer {
private server: Server;
private axiosInstance;
constructor() {
this.server = new Server({
name: "example-weather-server",
version: "0.1.0"
}, {
capabilities: {
resources: {},
tools: {}
}
});
// Configure axios with defaults
this.axiosInstance = axios.create({
baseURL: API_CONFIG.BASE_URL,
params: {
appid: API_KEY,
units: "metric"
}
});
this.setupHandlers();
this.setupErrorHandling();
}
private setupErrorHandling(): void {
this.server.onerror = (error) => {
console.error("[MCP Error]", error);
};
process.on('SIGINT', async () => {
await this.server.close();
process.exit(0);
});
}
private setupHandlers(): void {
this.setupResourceHandlers();
this.setupToolHandlers();
}
private setupResourceHandlers(): void {
// Implementation continues in next section
}
private setupToolHandlers(): void {
// Implementation continues in next section
}
async run(): Promise<void> {
const transport = new StdioServerTransport();
await this.server.connect(transport);
// Although this is just an informative message, we must log to stderr,
// to avoid interfering with MCP communication that happens on stdout
console.error("Weather MCP server running on stdio");
}
}
const server = new WeatherServer();
server.run().catch(console.error);
```
</Step>
<Step title="Add resource handlers">
Add this to the `setupResourceHandlers` method:
```typescript
private setupResourceHandlers(): void {
this.server.setRequestHandler(
ListResourcesRequestSchema,
async () => ({
resources: [{
uri: `weather://${API_CONFIG.DEFAULT_CITY}/current`,
name: `Current weather in ${API_CONFIG.DEFAULT_CITY}`,
mimeType: "application/json",
description: "Real-time weather data including temperature, conditions, humidity, and wind speed"
}]
})
);
this.server.setRequestHandler(
ReadResourceRequestSchema,
async (request) => {
const city = API_CONFIG.DEFAULT_CITY;
if (request.params.uri !== `weather://${city}/current`) {
throw new McpError(
ErrorCode.InvalidRequest,
`Unknown resource: ${request.params.uri}`
);
}
try {
const response = await this.axiosInstance.get<OpenWeatherResponse>(
API_CONFIG.ENDPOINTS.CURRENT,
{
params: { q: city }
}
);
const weatherData: WeatherData = {
temperature: response.data.main.temp,
conditions: response.data.weather[0].description,
humidity: response.data.main.humidity,
wind_speed: response.data.wind.speed,
timestamp: new Date().toISOString()
};
return {
contents: [{
uri: request.params.uri,
mimeType: "application/json",
text: JSON.stringify(weatherData, null, 2)
}]
};
} catch (error) {
if (axios.isAxiosError(error)) {
throw new McpError(
ErrorCode.InternalError,
`Weather API error: ${error.response?.data.message ?? error.message}`
);
}
throw error;
}
}
);
}
```
</Step>
<Step title="Add tool handlers">
Add these handlers to the `setupToolHandlers` method:
```typescript
private setupToolHandlers(): void {
this.server.setRequestHandler(
ListToolsRequestSchema,
async () => ({
tools: [{
name: "get_forecast",
description: "Get weather forecast for a city",
inputSchema: {
type: "object",
properties: {
city: {
type: "string",
description: "City name"
},
days: {
type: "number",
description: "Number of days (1-5)",
minimum: 1,
maximum: 5
}
},
required: ["city"]
}
}]
})
);
this.server.setRequestHandler(
CallToolRequestSchema,
async (request) => {
if (request.params.name !== "get_forecast") {
throw new McpError(
ErrorCode.MethodNotFound,
`Unknown tool: ${request.params.name}`
);
}
if (!isValidForecastArgs(request.params.arguments)) {
throw new McpError(
ErrorCode.InvalidParams,
"Invalid forecast arguments"
);
}
const city = request.params.arguments.city;
const days = Math.min(request.params.arguments.days || 3, 5);
try {
const response = await this.axiosInstance.get<{
list: OpenWeatherResponse[]
}>(API_CONFIG.ENDPOINTS.FORECAST, {
params: {
q: city,
cnt: days * 8 // API returns 3-hour intervals
}
});
const forecasts: ForecastDay[] = [];
for (let i = 0; i < response.data.list.length; i += 8) {
const dayData = response.data.list[i];
forecasts.push({
date: dayData.dt_txt?.split(' ')[0] ?? new Date().toISOString().split('T')[0],
temperature: dayData.main.temp,
conditions: dayData.weather[0].description
});
}
return {
content: [{
type: "text",
text: JSON.stringify(forecasts, null, 2)
}]
};
} catch (error) {
if (axios.isAxiosError(error)) {
return {
content: [{
type: "text",
text: `Weather API error: ${error.response?.data.message ?? error.message}`
}],
isError: true,
}
}
throw error;
}
}
);
}
```
</Step>
<Step title="Build and test">
```bash
npm run build
```
</Step>
</Steps>
## Connect to Claude Desktop
<Steps>
<Step title="Update Claude config">
If you didn't already connect to Claude Desktop during project setup, add to `claude_desktop_config.json`:
```json
{
"mcpServers": {
"weather": {
"command": "node",
"args": ["/path/to/weather-server/build/index.js"],
"env": {
"OPENWEATHER_API_KEY": "your-api-key",
}
}
}
}
```
</Step>
<Step title="Restart Claude">
1. Quit Claude completely
2. Start Claude again
3. Look for your weather server in the 🔌 menu
</Step>
</Steps>
## Try it out!
<AccordionGroup>
<Accordion title="Check Current Weather" active>
Ask Claude:
```
What's the current weather in San Francisco? Can you analyze the conditions?
```
</Accordion>
<Accordion title="Get a Forecast">
Ask Claude:
```
Can you get me a 5-day forecast for Tokyo and tell me if I should pack an umbrella?
```
</Accordion>
<Accordion title="Compare Weather">
Ask Claude:
```
Can you analyze the forecast for both Tokyo and San Francisco and tell me which city will be warmer this week?
```
</Accordion>
</AccordionGroup>
## Understanding the code
<Tabs>
<Tab title="Type Safety">
```typescript
interface WeatherData {
temperature: number;
conditions: string;
humidity: number;
wind_speed: number;
timestamp: string;
}
```
TypeScript adds type safety to our MCP server, making it more reliable and easier to maintain.
</Tab>
<Tab title="Resources">
```typescript
this.server.setRequestHandler(
ListResourcesRequestSchema,
async () => ({
resources: [{
uri: `weather://${DEFAULT_CITY}/current`,
name: `Current weather in ${DEFAULT_CITY}`,
mimeType: "application/json"
}]
})
);
```
Resources provide data that Claude can access as context.
</Tab>
<Tab title="Tools">
```typescript
{
name: "get_forecast",
description: "Get weather forecast for a city",
inputSchema: {
type: "object",
properties: {
city: { type: "string" },
days: { type: "number" }
}
}
}
```
Tools let Claude take actions through your server with type-safe inputs.
</Tab>
</Tabs>
## Best practices
<CardGroup cols={1}>
<Card title="Error Handling" icon="shield">
When a tool encounters an error, return the error message with `isError: true`, so the model can self-correct:
```typescript
try {
const response = await axiosInstance.get(...);
} catch (error) {
if (axios.isAxiosError(error)) {
return {
content: {
mimeType: "text/plain",
text: `Weather API error: ${error.response?.data.message ?? error.message}`
},
isError: true,
}
}
throw error;
}
```
For other handlers, throw an error, so the application can notify the user:
```typescript
try {
const response = await this.axiosInstance.get(...);
} catch (error) {
if (axios.isAxiosError(error)) {
throw new McpError(
ErrorCode.InternalError,
`Weather API error: ${error.response?.data.message}`
);
}
throw error;
}
```
</Card>
<Card title="Type Validation" icon="check">
```typescript
function isValidForecastArgs(args: any): args is GetForecastArgs {
return (
typeof args === "object" &&
args !== null &&
"city" in args &&
typeof args.city === "string"
);
}
```
<Tip>You can also use libraries like [Zod](https://zod.dev/) to perform this validation automatically.</Tip>
</Card>
</CardGroup>
## Available transports
While this guide uses stdio to run the MCP server as a local process, MCP supports other [transports](/docs/concepts/transports) as well.
## Troubleshooting
<Info>
The following troubleshooting tips are for macOS. Guides for other platforms are coming soon.
</Info>
### Build errors
```bash
# Check TypeScript version
npx tsc --version
# Clean and rebuild
rm -rf build/
npm run build
```
### Runtime errors
Look for detailed error messages in the Claude Desktop logs:
```bash
# Monitor logs
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```
### Type errors
```bash
# Check types without building
npx tsc --noEmit
```
## Next steps
<CardGroup cols={2}>
<Card title="Architecture overview" icon="sitemap" href="/docs/concepts/architecture">
Learn more about the MCP architecture
</Card>
<Card title="TypeScript SDK" icon="square-js" href="https://github.com/modelcontextprotocol/typescript-sdk">
Check out the TypeScript SDK on GitHub
</Card>
</CardGroup>
<Note>
Need help? Ask Claude! Since it has access to the MCP SDK documentation, it can help you debug issues and suggest improvements to your server.
</Note>
# Debugging
A comprehensive guide to debugging Model Context Protocol (MCP) integrations
Effective debugging is essential when developing MCP servers or integrating them with applications. This guide covers the debugging tools and approaches available in the MCP ecosystem.
<Info>
This guide is for macOS. Guides for other platforms are coming soon.
</Info>
## Debugging tools overview
MCP provides several tools for debugging at different levels:
1. **MCP Inspector**
* Interactive debugging interface
* Direct server testing
* See the [Inspector guide](/docs/tools/inspector) for details
2. **Claude Desktop Developer Tools**
* Integration testing
* Log collection
* Chrome DevTools integration
3. **Server Logging**
* Custom logging implementations
* Error tracking
* Performance monitoring
## Debugging in Claude Desktop
### Checking server status
The Claude.app interface provides basic server status information:
1. Click the 🔌 icon to view:
* Connected servers
* Available prompts and resources
2. Click the 🔨 icon to view:
* Tools made available to the model
### Viewing logs
Review detailed MCP logs from Claude Desktop:
```bash
# Follow logs in real-time
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```
The logs capture:
* Server connection events
* Configuration issues
* Runtime errors
* Message exchanges
### Using Chrome DevTools
Access Chrome's developer tools inside Claude Desktop to investigate client-side errors:
1. Enable DevTools:
```bash
jq '.allowDevTools = true' ~/Library/Application\ Support/Claude/developer_settings.json > tmp.json \
&& mv tmp.json ~/Library/Application\ Support/Claude/developer_settings.json
```
2. Open DevTools: `Command-Option-Shift-i`
Note: You'll see two DevTools windows:
* Main content window
* App title bar window
Use the Console panel to inspect client-side errors.
Use the Network panel to inspect:
* Message payloads
* Connection timing
## Common issues
### Environment variables
MCP servers inherit only a subset of environment variables automatically, like `USER`, `HOME`, and `PATH`.
To override the default variables or provide your own, you can specify an `env` key in `claude_desktop_config.json`:
```json
{
"myserver": {
"command": "mcp-server-myapp",
"env": {
"MYAPP_API_KEY": "some_key",
}
}
}
```
### Server initialization
Common initialization problems:
1. **Path Issues**
* Incorrect server executable path
* Missing required files
* Permission problems
2. **Configuration Errors**
* Invalid JSON syntax
* Missing required fields
* Type mismatches
3. **Environment Problems**
* Missing environment variables
* Incorrect variable values
* Permission restrictions
### Connection problems
When servers fail to connect:
1. Check Claude Desktop logs
2. Verify server process is running
3. Test standalone with [Inspector](/docs/tools/inspector)
4. Verify protocol compatibility
## Implementing logging
### Server-side logging
When building a server that uses the local stdio [transport](/docs/concepts/transports), all messages logged to stderr (standard error) will be captured by the host application (e.g., Claude Desktop) automatically.
<Warning>
Local MCP servers should not log messages to stdout (standard out), as this will interfere with protocol operation.
</Warning>
For all [transports](/docs/concepts/transports), you can also provide logging to the client by sending a log message notification:
<Tabs>
<Tab title="Python">
```python
server.request_context.session.send_log_message(
level="info",
data="Server started successfully",
)
```
</Tab>
<Tab title="TypeScript">
```typescript
server.sendLoggingMessage({
level: "info",
data: "Server started successfully",
});
```
</Tab>
</Tabs>
Important events to log:
* Initialization steps
* Resource access
* Tool execution
* Error conditions
* Performance metrics
### Client-side logging
In client applications:
1. Enable debug logging
2. Monitor network traffic
3. Track message exchanges
4. Record error states
## Debugging workflow
### Development cycle
1. Initial Development
* Use [Inspector](/docs/tools/inspector) for basic testing
* Implement core functionality
* Add logging points
2. Integration Testing
* Test in Claude Desktop
* Monitor logs
* Check error handling
### Testing changes
To test changes efficiently:
* **Configuration changes**: Restart Claude Desktop
* **Server code changes**: Use Command-R to reload
* **Quick iteration**: Use [Inspector](/docs/tools/inspector) during development
## Best practices
### Logging strategy
1. **Structured Logging**
* Use consistent formats
* Include context
* Add timestamps
* Track request IDs
2. **Error Handling**
* Log stack traces
* Include error context
* Track error patterns
* Monitor recovery
3. **Performance Tracking**
* Log operation timing
* Monitor resource usage
* Track message sizes
* Measure latency
### Security considerations
When debugging:
1. **Sensitive Data**
* Sanitize logs
* Protect credentials
* Mask personal information
2. **Access Control**
* Verify permissions
* Check authentication
* Monitor access patterns
## Getting help
When encountering issues:
1. **First Steps**
* Check server logs
* Test with [Inspector](/docs/tools/inspector)
* Review configuration
* Verify environment
2. **Support Channels**
* GitHub issues
* GitHub discussions
3. **Providing Information**
* Log excerpts
* Configuration files
* Steps to reproduce
* Environment details
## Next steps
<CardGroup cols={2}>
<Card title="MCP Inspector" icon="magnifying-glass" href="/docs/tools/inspector">
Learn to use the MCP Inspector
</Card>
</CardGroup>
# Inspector
In-depth guide to using the MCP Inspector for testing and debugging Model Context Protocol servers
The [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is an interactive developer tool for testing and debugging MCP servers. While the [Debugging Guide](/docs/tools/debugging) covers the Inspector as part of the overall debugging toolkit, this document provides a detailed exploration of the Inspector's features and capabilities.
## Getting started
### Installation and basic usage
The Inspector runs directly through `npx` without requiring installation:
```bash
npx @modelcontextprotocol/inspector <command>
```
```bash
npx @modelcontextprotocol/inspector <command> <arg1> <arg2>
```
#### Inspecting servers from NPM or PyPi
A common way to start server packages from [NPM](https://npmjs.com) or [PyPi](https://pypi.com).
<Tabs>
<Tab title="NPM package">
```bash
npx -y @modelcontextprotocol/inspector npx <package-name> <args>
# For example
npx -y @modelcontextprotocol/inspector npx server-postgres postgres://127.0.0.1/testdb
```
</Tab>
<Tab title="PyPi package">
```bash
npx @modelcontextprotocol/inspector uvx <package-name> <args>
# For example
npx @modelcontextprotocol/inspector uvx mcp-server-git --repository ~/code/mcp/servers.git
```
</Tab>
</Tabs>
#### Inspecting locally developed servers
To inspect servers locally developed or downloaded as a repository, the most common
way is:
<Tabs>
<Tab title="TypeScript">
```bash
npx @modelcontextprotocol/inspector node path/to/server/index.js args...
```
</Tab>
<Tab title="Python">
```bash
npx @modelcontextprotocol/inspector \
uv \
--directory path/to/server \
run \
package-name \
args...
```
</Tab>
</Tabs>
Please carefully read any attached README for the most accurate instructions.
## Feature overview
<Frame caption="The MCP Inspector interface">
<img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/mcp-inspector.png" />
</Frame>
The Inspector provides several features for interacting with your MCP server:
### Server connection pane
* Allows selecting the [transport](/docs/concepts/transports) for connecting to the server
* For local servers, supports customizing the command-line arguments and environment
### Resources tab
* Lists all available resources
* Shows resource metadata (MIME types, descriptions)
* Allows resource content inspection
* Supports subscription testing
### Prompts tab
* Displays available prompt templates
* Shows prompt arguments and descriptions
* Enables prompt testing with custom arguments
* Previews generated messages
### Tools tab
* Lists available tools
* Shows tool schemas and descriptions
* Enables tool testing with custom inputs
* Displays tool execution results
### Notifications pane
* Presents all logs recorded from the server
* Shows notifications received from the server
## Best practices
### Development workflow
1. Start Development
* Launch Inspector with your server
* Verify basic connectivity
* Check capability negotiation
2. Iterative testing
* Make server changes
* Rebuild the server
* Reconnect the Inspector
* Test affected features
* Monitor messages
3. Test edge cases
* Invalid inputs
* Missing prompt arguments
* Concurrent operations
* Verify error handling and error responses
## Next steps
<CardGroup cols={2}>
<Card title="Inspector Repository" icon="github" href="https://github.com/modelcontextprotocol/inspector">
Check out the MCP Inspector source code
</Card>
<Card title="Debugging Guide" icon="bug" href="/docs/tools/debugging">
Learn about broader debugging strategies
</Card>
</CardGroup>
# Introduction
Get started with the Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
## Get started with MCP
Choose the path that best fits your needs:
<CardGroup cols={1}>
<Card title="Quickstart" icon="bolt" href="/quickstart">
The fastest way to see MCP in action—connect example servers to Claude Desktop
</Card>
<Card title="Build your first server (Python)" icon="python" href="/docs/first-server/python">
Create a simple MCP server in Python to understand the basics
</Card>
<Card title="Build your first server (TypeScript)" icon="square-js" href="/docs/first-server/typescript">
Create a simple MCP server in TypeScript to understand the basics
</Card>
</CardGroup>
## Development tools
Essential tools for building and debugging MCP servers:
<CardGroup cols={2}>
<Card title="Debugging Guide" icon="bug" href="/docs/tools/debugging">
Learn how to effectively debug MCP servers and integrations
</Card>
<Card title="MCP Inspector" icon="magnifying-glass" href="/docs/tools/inspector">
Test and inspect your MCP servers with our interactive debugging tool
</Card>
</CardGroup>
## Explore MCP
Dive deeper into MCP's core concepts and capabilities:
<CardGroup cols={2}>
<Card title="Core Architecture" icon="sitemap" href="/docs/concepts/architecture">
Understand how MCP connects clients, servers, and LLMs
</Card>
<Card title="Resources" icon="database" href="/docs/concepts/resources">
Expose data and content from your servers to LLMs
</Card>
<Card title="Prompts" icon="message" href="/docs/concepts/prompts">
Create reusable prompt templates and workflows
</Card>
<Card title="Tools" icon="wrench" href="/docs/concepts/tools">
Enable LLMs to perform actions through your server
</Card>
<Card title="Sampling" icon="robot" href="/docs/concepts/sampling">
Let your servers request completions from LLMs
</Card>
<Card title="Transports" icon="network-wired" href="/docs/concepts/transports">
Learn about MCP's communication mechanism
</Card>
</CardGroup>
## Contributing
Want to contribute? Check out [@modelcontextprotocol](https://github.com/modelcontextprotocol) on GitHub to join our growing community of developers building with MCP.
# Quickstart
Get started with MCP in less than 5 minutes
MCP is a protocol that enables secure connections between host applications, such as [Claude Desktop](https://claude.ai/download), and local services. In this quickstart guide, you'll learn how to:
* Set up a local SQLite database
* Connect Claude Desktop to it through MCP
* Query and analyze your data securely
<Note>
While this guide focuses on using Claude Desktop as an example MCP host, the protocol is open and can be integrated by any application. IDEs, AI tools, and other software can all use MCP to connect to local integrations in a standardized way.
</Note>
<Warning>
Claude Desktop's MCP support is currently in developer preview and only supports connecting to local MCP servers running on your machine. Remote MCP connections are not yet supported. This integration is only available in the Claude Desktop app, not the Claude web interface (claude.ai).
</Warning>
## How MCP works
MCP (Model Context Protocol) is an open protocol that enables secure, controlled interactions between AI applications and local or remote resources. Let's break down how it works, then look at how we'll use it in this guide.
### General Architecture
At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:
```mermaid
flowchart LR
subgraph "Your Computer"
Host["MCP Host\n(Claude, IDEs, Tools)"]
S1["MCP Server A"]
S2["MCP Server B"]
S3["MCP Server C"]
Host <-->|"MCP Protocol"| S1
Host <-->|"MCP Protocol"| S2
Host <-->|"MCP Protocol"| S3
S1 <--> R1[("Local\nResource A")]
S2 <--> R2[("Local\nResource B")]
end
subgraph "Internet"
S3 <-->|"Web APIs"| R3[("Remote\nResource C")]
end
```
* **MCP Hosts**: Programs like Claude Desktop, IDEs, or AI tools that want to access resources through MCP
* **MCP Clients**: Protocol clients that maintain 1:1 connections with servers
* **MCP Servers**: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
* **Local Resources**: Your computer's resources (databases, files, services) that MCP servers can securely access
* **Remote Resources**: Resources available over the internet (e.g., through APIs) that MCP servers can connect to
### In This Guide
For this quickstart, we'll implement a focused example using SQLite:
```mermaid
flowchart LR
subgraph "Your Computer"
direction LR
Claude["Claude Desktop"]
MCP["SQLite MCP Server"]
DB[(SQLite Database\n~/test.db)]
Claude <-->|"MCP Protocol\n(Queries & Results)"| MCP
MCP <-->|"Local Access\n(SQL Operations)"| DB
end
```
1. Claude Desktop acts as our MCP client
2. A SQLite MCP Server provides secure database access
3. Your local SQLite database stores the actual data
The communication between the SQLite MCP server and your local SQLite database happens entirely on your machine—your SQLite database is not exposed to the internet. The Model Context Protocol ensures that Claude Desktop can only perform approved database operations through well-defined interfaces. This gives you a secure way to let Claude analyze and interact with your local data while maintaining complete control over what it can access.
## Prerequisites
* macOS or Windows
* The latest version of [Claude Desktop](https://claude.ai/download) installed
* [uv](https://docs.astral.sh/uv/) 0.4.18 or higher (`uv --version` to check)
* Git (`git --version` to check)
* SQLite (`sqlite3 --version` to check)
<AccordionGroup>
<Accordion title="Installing prerequisites (macOS)">
```bash
# Using Homebrew
brew install uv git sqlite3
# Or download directly:
# uv: https://docs.astral.sh/uv/
# Git: https://git-scm.com
# SQLite: https://www.sqlite.org/download.html
```
</Accordion>
<Accordion title="Installing prerequisites (Windows)">
```powershell
# Using winget
winget install --id=astral-sh.uv -e
winget install git.git sqlite.sqlite
# Or download directly:
# uv: https://docs.astral.sh/uv/
# Git: https://git-scm.com
# SQLite: https://www.sqlite.org/download.html
```
</Accordion>
</AccordionGroup>
## Installation
<Tabs>
<Tab title="macOS">
<Steps>
<Step title="Create a sample database">
Let's create a simple SQLite database for testing:
```bash
# Create a new SQLite database
sqlite3 ~/test.db <<EOF
CREATE TABLE products (
id INTEGER PRIMARY KEY,
name TEXT,
price REAL
);
INSERT INTO products (name, price) VALUES
('Widget', 19.99),
('Gadget', 29.99),
('Gizmo', 39.99),
('Smart Watch', 199.99),
('Wireless Earbuds', 89.99),
('Portable Charger', 24.99),
('Bluetooth Speaker', 79.99),
('Phone Stand', 15.99),
('Laptop Sleeve', 34.99),
('Mini Drone', 299.99),
('LED Desk Lamp', 45.99),
('Keyboard', 129.99),
('Mouse Pad', 12.99),
('USB Hub', 49.99),
('Webcam', 69.99),
('Screen Protector', 9.99),
('Travel Adapter', 27.99),
('Gaming Headset', 159.99),
('Fitness Tracker', 119.99),
('Portable SSD', 179.99);
EOF
```
</Step>
<Step title="Configure Claude Desktop">
Open your Claude Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor.
For example, if you have [VS Code](https://code.visualstudio.com/) installed:
```bash
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
```
Add this configuration (replace YOUR\_USERNAME with your actual username):
```json
{
"mcpServers": {
"sqlite": {
"command": "uvx",
"args": ["mcp-server-sqlite", "--db-path", "/Users/YOUR_USERNAME/test.db"]
}
}
}
```
This tells Claude Desktop:
1. There's an MCP server named "sqlite"
2. Launch it by running `uvx mcp-server-sqlite`
3. Connect it to your test database
Save the file, and restart **Claude Desktop**.
</Step>
</Steps>
</Tab>
<Tab title="Windows">
<Steps>
<Step title="Create a sample database">
Let's create a simple SQLite database for testing:
```powershell
# Create a new SQLite database
$sql = @'
CREATE TABLE products (
id INTEGER PRIMARY KEY,
name TEXT,
price REAL
);
INSERT INTO products (name, price) VALUES
('Widget', 19.99),
('Gadget', 29.99),
('Gizmo', 39.99),
('Smart Watch', 199.99),
('Wireless Earbuds', 89.99),
('Portable Charger', 24.99),
('Bluetooth Speaker', 79.99),
('Phone Stand', 15.99),
('Laptop Sleeve', 34.99),
('Mini Drone', 299.99),
('LED Desk Lamp', 45.99),
('Keyboard', 129.99),
('Mouse Pad', 12.99),
('USB Hub', 49.99),
('Webcam', 69.99),
('Screen Protector', 9.99),
('Travel Adapter', 27.99),
('Gaming Headset', 159.99),
('Fitness Tracker', 119.99),
('Portable SSD', 179.99);
'@
cd ~
& sqlite3 test.db $sql
```
</Step>
<Step title="Configure Claude Desktop">
Open your Claude Desktop App configuration at `%APPDATA%\Claude\claude_desktop_config.json` in a text editor.
For example, if you have [VS Code](https://code.visualstudio.com/) installed:
```powershell
code $env:AppData\Claude\claude_desktop_config.json
```
Add this configuration (replace YOUR\_USERNAME with your actual username):
```json
{
"mcpServers": {
"sqlite": {
"command": "uvx",
"args": [
"mcp-server-sqlite",
"--db-path",
"C:\\Users\\YOUR_USERNAME\\test.db"
]
}
}
}
```
This tells Claude Desktop:
1. There's an MCP server named "sqlite"
2. Launch it by running `uvx mcp-server-sqlite`
3. Connect it to your test database
Save the file, and restart **Claude Desktop**.
</Step>
</Steps>
</Tab>
</Tabs>
## Test it out
Let's verify everything is working. Try sending this prompt to Claude Desktop:
```
Can you connect to my SQLite database and tell me what products are available, and their prices?
```
Claude Desktop will:
1. Connect to the SQLite MCP server
2. Query your local database
3. Format and present the results
<Frame caption="Claude Desktop successfully queries our SQLite database 🎉">
<img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-screenshot.png" alt="Example Claude Desktop conversation showing database query results" />
</Frame>
## What's happening under the hood?
When you interact with Claude Desktop using MCP:
1. **Server Discovery**: Claude Desktop connects to your configured MCP servers on startup
2. **Protocol Handshake**: When you ask about data, Claude Desktop:
* Identifies which MCP server can help (sqlite in this case)
* Negotiates capabilities through the protocol
* Requests data or actions from the MCP server
3. **Interaction Flow**:
```mermaid
sequenceDiagram
participant C as Claude Desktop
participant M as MCP Server
participant D as SQLite DB
C->>M: Initialize connection
M-->>C: Available capabilities
C->>M: Query request
M->>D: SQL query
D-->>M: Results
M-->>C: Formatted results
```
4. **Security**:
* MCP servers only expose specific, controlled capabilities
* MCP servers run locally on your machine, and the resources they access are not exposed to the internet
* Claude Desktop requires user confirmation for sensitive operations
## Try these examples
Now that MCP is working, try these increasingly powerful examples:
<AccordionGroup>
<Accordion title="Basic Queries" active>
```
What's the average price of all products in the database?
```
</Accordion>
<Accordion title="Data Analysis">
```
Can you analyze the price distribution and suggest any pricing optimizations?
```
</Accordion>
<Accordion title="Complex Operations">
```
Could you help me design and create a new table for storing customer orders?
```
</Accordion>
</AccordionGroup>
## Add more capabilities
Want to give Claude Desktop more local integration capabilities? Add these servers to your configuration:
<Note>
Note that these MCP servers will require [Node.js](https://nodejs.org/en) to be installed on your machine.
</Note>
<AccordionGroup>
<Accordion title="File System Access" icon="folder-open">
Add this to your config to let Claude Desktop read and analyze files:
```json
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/YOUR_USERNAME/Desktop"]
}
```
</Accordion>
<Accordion title="PostgreSQL Connection" icon="database">
Connect Claude Desktop to your PostgreSQL database:
```json
"postgres": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://localhost/mydb"]
}
```
</Accordion>
</AccordionGroup>
## More MCP Clients
While this guide demonstrates MCP using Claude Desktop as a client, several other applications support MCP integration:
<CardGroup cols={2}>
<Card title="Zed Editor" icon="pen-to-square" href="https://zed.dev">
A high-performance, multiplayer code editor with built-in MCP support for AI-powered coding assistance
</Card>
<Card title="Cody" icon="magnifying-glass" href="https://sourcegraph.com/cody">
Code intelligence platform featuring MCP integration for enhanced code search and analysis capabilities
</Card>
</CardGroup>
Each host application may implement MCP features differently or support different capabilities. Check their respective documentation for specific setup instructions and supported features.
## Troubleshooting
<AccordionGroup>
<Accordion title="Nothing showing up in Claude Desktop?">
1. Check if MCP is enabled:
* Click the 🔌 icon in Claude Desktop, next to the chat box
* Expand "Installed MCP Servers"
* You should see your configured servers
2. Verify your config:
* From Claude Desktop, go to Claude > Settings…
* Open the "Developer" tab to see your configuration
3. Restart Claude Desktop completely:
* Quit the app (not just close the window)
* Start it again
</Accordion>
<Accordion title="MCP or database errors?">
1. Check Claude Desktop's logs:
```bash
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
```
2. Verify database access:
```bash
# Test database connection
sqlite3 ~/test.db ".tables"
```
3. Common fixes:
* Check file paths in your config
* Verify database file permissions
* Ensure SQLite is installed properly
</Accordion>
</AccordionGroup>
## Next steps
<CardGroup cols={2}>
<Card title="Build your first MCP server" icon="code" href="/docs/first-server/python">
Create your own MCP servers to give your LLM clients new capabilities.
</Card>
<Card title="Explore examples" icon="github" href="https://github.com/modelcontextprotocol/servers">
Browse our collection of example servers to see what's possible.
</Card>
</CardGroup>
```