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python-mcp-server-generator

Lets your agent create a Python server to communicate with AI clients. It exposes functions and data as tools the AI can use.

What it does

This program creates a Python 'server' that allows other AIs (like Claude) to use functions you define. It exposes your functions, a database, or an API so the AI can interact with them.

When to use it

  • ✓When you want an AI to access data from a database.
  • ✓When you want an AI to execute a specific function on your computer.
  • ✓When you want to create a bridge between an AI and an existing API.

Example of what to ask your AI

“Create a Python server that allows me to divide numbers using AI.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/github/awesome-copilot/blob/main/skills/python-mcp-server-generator/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/github/awesome-copilot/blob/main/skills/python-mcp-server-generator/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

You write Python code defining the functions the AI can use. The program organizes these functions and prepares them so the AI can call them and receive results.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add github/awesome-copilot --skill python-mcp-server-generator

Or download just the file:

curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/python-mcp-server-generator/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Python MCP Server Generator

Create a Model Context Protocol (MCP) server in Python with the official MCP Python SDK v2. SDK v2 supports the MCP specification revision 2026-07-28. The same server also serves clients that use earlier revisions.

Workflow

Copy this checklist and track your progress:

  • Step 1: Create the project.
  • Step 2: Write the server.
  • Step 3: Write the tests.
  • Step 4: Run the tests until they pass.
  • Step 5: Configure the client.

Step 1: Create the project

Run these commands:

uv init --app --no-package project-name
cd project-name
uv add "mcp[cli]>=2,<3"
uv add --dev pytest

Then delete main.py. The server goes in server.py.

  • Without --no-package, uv makes a src/ package layout, and the tests cannot import server.py.
  • uv init makes the .gitignore file. Do not write a different one.

Step 2: Write the server

Start from this template:

from mcp.server import MCPServer
from mcp.server.mcpserver.exceptions import ToolError

mcp = MCPServer("Demo", version="0.1.0")


@mcp.tool()
def divide(a: float, b: float) -> float:
    """Divide a by b."""
    if b == 0:
        raise ToolError("b must not be zero.")
    return a / b


if __name__ == "__main__":
    mcp.run()  # stdio is the default transport

Then change the template for the request of the user:

  1. Replace divide with the tools that the user needs.
  2. Give each tool full type hints and a docstring. The SDK makes the input schema from the type hints. The docstring becomes the tool description.
  3. Return a Pydantic model or a TypedDict when the client needs machine-readable data. The SDK makes the output schema from the return type.
  4. Use async def for I/O. The SDK runs a sync tool on a worker thread.
  5. Mark a read-only tool with @mcp.tool(annotations=ToolAnnotations(read_only_hint=True)) (from mcp.types). Use destructive_hint=True for a destructive tool.
  6. Add resources (@mcp.resource("users://{user_id}")) and prompts (@mcp.prompt()) only when the user asks for them.

Use stdio by default. Read references/streamable-http.md only when the user asks for a remote server or an HTTP server.

Step 3: Write the tests

Put test_server.py next to server.py. Write a test for each tool and for each ToolError:

import pytest

from mcp import Client

from server import mcp


@pytest.mark.anyio
async def test_divide():
    async with Client(mcp) as client:
        result = await client.call_tool("divide", {"a": 6, "b": 3})
        assert result.structured_content == {"result": 2.0}


@pytest.mark.anyio
async def test_divide_by_zero():
    async with Client(mcp) as client:
        result = await client.call_tool("divide", {"a": 1, "b": 0})
        assert result.is_error
        assert "must not be zero" in result.content[0].text
  • The in-memory Client needs no subprocess and no port. It connects with the 2026-07-28 revision.
  • The mcp package installs anyio, which supplies the anyio marker. Do not add pytest-asyncio.

Step 4: Run the tests until they pass

  1. Run uv run pytest.
  2. If a test fails, read the error. Fix the server or the test.
  3. Run uv run pytest again.
  4. Continue only when all tests pass.

Step 5: Configure the client

For VS Code, write .vscode/mcp.json:

{
  "servers": {
    "demo": {
      "type": "stdio",
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/project", "run", "server.py"]
    }
  }
}

For Claude Desktop, run uv run mcp install server.py.

Tell the user these commands:

  • uv run server.py starts the stdio server. The server waits for a host on stdin and prints nothing.
  • uv run mcp dev server.py opens the MCP Inspector. The Inspector needs Node.js.

Gotchas

SDK v2 and the 2026-07-28 revision changed many SDK v1 patterns. Do not copy SDK v1 examples.

  • SDK v2 removed FastMCP and mcp.server.fastmcp. Import the server class with from mcp.server import MCPServer.
  • Import Context, Image, Audio, Resolve, Elicit, ElicitationResult, AcceptedElicitation, Message, UserMessage, and AssistantMessage from mcp.server.mcpserver.

…

FAQ

What is MCP?

MCP stands for Model Context Protocol. It's a standardized way for computer programs (servers) and AIs to communicate with each other.

What is a 'server' in this context?

It's a program that 'listens' and is ready to receive requests from the AI, perform tasks, and return results.

Can I use this to create an AI client?

No, this program is specifically for creating the 'server' that the AI will use, not the program the AI itself runs on.

Details

Original description by the author

Use this skill when the user wants to create a new Model Context Protocol (MCP) server in Python. Also use it to expose Python functions, an API, or a database to AI clients as MCP tools. The skill makes a uv project with the MCP Python SDK v2 (MCPServer), typed tools, in-memory tests, and client configuration. Do not use it for an MCP client or for a server in another language.

repo: github/awesome-copilot

Popularity

GitHub stars
40k
Ranking position
#5294
License
MIT
Updated
10/15/2026

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