langchain-mcp: Model Context Protocol tool support for LangChain

by rectalogic · MCP Server · ★ 205

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 58/100

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.

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.

Quick Facts

Stars205
Forks24
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score58/100
Open Issues1
Last Updated2025-04-02
Created2024-11-26
Platformsmcp, python
Est. Tokens~12k

About langchain-mcp

langchain-mcp Note: langchain now has a more official implementation langchain-mcp-adapters Model Context Protocol tool calling support in LangChain. Create a with an , then and to get the list of s. Example: https://github.com/rectalogic/langchain-mcp/blob/8fa8445a24755bf91789f52718c32361ed916f46/tests/demo.py#L34-L43 Demo You can run the demo against Groq :

Compatible Skills

These tools work well together with langchain-mcp for enhanced workflows:

  • WHartTest — semantic(0.48)+shared_fw(langchain)+same_lang+similar_pop+shared_platform (55%)
  • investor-agent — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (54%)
  • mcp-crash-course — semantic(0.30)+shared_fw(langchain)+same_lang+similar_pop+shared_platform (49%)
  • mcp-server-okppt — semantic(0.48)+same_lang+similar_pop+shared_platform (47%)

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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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