while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools

Mar 21, 2026

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The official MCP Python SDK provides FastMCP

This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup

- [ ] Tools enable complete workflows

* This server provides tools to interact with Example API

const data = await apiRequest(params.limit

textContent = JSON.stringify(output

content: [{ type: text

│ └── constants.ts # Shared constants (API_URL

- **DO NOT use**: Old deprecated APIs such as `server.tool()`

content: [{ type: text

<question>Look for pull requests that modified files in the /api directory and were merged between January 1 and January 31

After inspecting the content

- Goal: identify specific content (e.g.

After understanding the API and tools

- Look at old content (e.g.

return { content: [{ type: text

- Store API keys in environment variables

- **Python**: `{service}_mcp` (e.g.

Prioritize comprehensive API coverage. List endpoints to implement

- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization

Review the service's API documentation to identify key endpoints

- **Language**: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code

Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused

Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks

- DO NOT use: Old deprecated APIs such as `server.tool()`

- Python: `{service}_mcp` (e.g.

- Language: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code