Maintainer of this skill?
Automated security scan — not a certification.
The official Python SDK for Model Context Protocol servers and clients
# Add to your Claude Code skills
git clone https://github.com/modelcontextprotocol/python-sdkGuides for using mcp servers skills like python-sdk.
Last scanned: 8/4/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-08-04T06:27:16.783Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how python-sdk compares with popular alternatives.
python-sdk is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by modelcontextprotocol. The official Python SDK for Model Context Protocol servers and clients. It has 24,736 GitHub stars.
Yes. python-sdk passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/modelcontextprotocol/python-sdk" and add it to your Claude Code skills directory (see the Installation section above).
python-sdk is primarily written in Python. It is open-source under modelcontextprotocol on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh python-sdk against similar tools.
No comments yet. Be the first to share your thoughts!
Top skills in this category by stars
Based on votes and bookmarks from developers who liked this skill
From $49 · 30 days · terms & refunds
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
Python implementation of the Model Context Protocol (MCP)
The documentation lives at https://py.sdk.modelcontextprotocol.io/.
It has a Get started guide, What's new in v2, the API reference, and the migration guide.
The Model Context Protocol lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but designed for LLM interactions. With this SDK you can:
Python 3.11+.
uv add "mcp[cli]" # or: pip install "mcp[cli]"
The cli extra adds the mcp command-line tool (mcp dev, mcp run, mcp install) on top of the SDK; install plain mcp if you don't need it. For one-off commands, uv run --with "mcp[cli]" mcp ... works without a project.
Create a server.py:
from mcp.server import MCPServer
mcp = MCPServer("Demo")
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
@mcp.resource("greeting://{name}")
def greeting(name: str) -> str:
"""Greet someone by name."""
return f"Hello, {name}!"
Full example: docs_src/index/tutorial001.py
That's a complete MCP server: one tool, one templated resource. Open it in the MCP Inspector:
uv run mcp dev server.py
Call add with a=1, b=2 and you get 3 back.
Notice what you did not write: no JSON Schema (a: int, b: int is the schema), no request parsing, no validation code, no protocol handling. Two type-hinted Python functions and a docstring.
Get started takes it from here.
The same package is a full MCP client. Serve server.py over HTTP:
uv run mcp run server.py --transport streamable-http
then point a Client at it:
import asyncio
from mcp import Client
async def main() -> None:
async with Client("http://localhost:8000/mcp") as client:
result = await client.call_tool("add", {"a": 1, "b": 2})
print(result.structured_content) # {'result': 3}
asyncio.run(main())
A URL means Streamable HTTP, the transport you deploy. Client can also launch a local server as a stdio subprocess or take any custom transport; Clients has the rest.
We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the contributing guide to get started.
This project is licensed under the MIT License. See the LICENSE file for details.