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FastMCP can automatically inject a `Context` parameter into tools for advanced capabilities like logging

progress reporting

users = data.get(users

[])

total = data.get(total

async def _make_api_request(endpoint: str

method: str = GET

project management

and data export capabilities.

This server provides tools to interact with Example API

including user search

return {id: data[id]

name: data[name]}

async def _make_api_request(endpoint: str

method: str = GET

data = await api_request(limit=params.limit

offset=params.offset)

- Return complete

structured data suitable for programmatic processing

- Omit verbose metadata (e.g.

show only one profile image URL

The official MCP Python SDK provides FastMCP

a high-level framework for building MCP servers. It provides:

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

tool registration patterns

- [ ] Tools enable complete workflows

not just API endpoint wrappers

Expose data as resources for efficient

URI-based access:

* project management

and data export capabilities.

* This server provides tools to interact with Example API

including user search

const truncatedData = data.slice(0

Math.max(1

- Return complete

structured data suitable for programmatic processing

const data = await apiRequest(params.limit

params.offset);

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

CHARACTER_LIMIT

Productivity

- The `register*` methods provide better type safety

automatic schema handling

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

`server.setRequestHandler(ListToolsRequestSchema

Productivity

2. Load each task instruction and in parallel using the MCP server and tools

identify the correct answer by attempting to solve the task YOURSELF