python-sdk: The official Python SDK for Model Context Protocol servers and clients

by modelcontextprotocol · MCP Server · ★ 24.8k

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Security audit verdict: SAFE · quality 71/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

mcpmcp-clientmcp-serverpython

Quick Facts

Stars24,771
Forks4,101
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score71/100
Open Issues395
Last Updated2026-11-05
Created2024-09-24
Platformsmcp, python
Est. Tokens~25k

About python-sdk

MCP Python SDK Python implementation of the Model Context Protocol (MCP) [![PyPI][pypi-badge]][pypi-url] [![MIT licensed][mit-badge]][mit-url] [![Python Version][python-badge]][python-url] [![Documentation][docs-badge]][docs-url] [![Protocol][protocol-badge]][protocol-url] [![Specification][spec-badge]][spec-url] [!NOTE] This README documents v1.x of the MCP Python SDK (the current stable...

Compatible Skills

These tools work well together with python-sdk for enhanced workflows:

  • fastmcp — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (62%)
  • mcp-agent — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (52%)
  • mcp-server-milvus — semantic(0.40)+complementary+same_lang+shared_platform (49%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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