langchainjs-mcp-adapters: THIS REPO HAS MOVED TO Adapters for integrating Model Context Protocol tools with LangChain.js applications

by langchain-ai · MCP Server · ★ 248

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

Security audit verdict: SAFE · quality 49/100

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.

agent-toolsai-toolsjavascriptlangchainlangchainjsllm-agentsllm-integrationllm-toolsmcpmodel-context-protocol

Quick Facts

Stars248
Forks36
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score49/100
Last Updated2025-05-16
Created2025-03-03
Platformsmcp, node
Est. Tokens~159k

About langchainjs-mcp-adapters

LangChain.js MCP Adapters [!IMPORTANT] This package has been migrated into the LangChainJS monorepo. This library provides a lightweight wrapper to allow Model Context Protocol (MCP) services to be used with LangChain.js. This project has moved. For a current description of this project, please see the up-to-date README at the project's new location.

Compatible Skills

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

  • zeek.ai — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • palico-ai — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)
  • azure-ai-travel-agents — semantic(0.23)+shared_fw(langchain)+same_lang+similar_pop+shared_platform (51%)
  • mcp-agent-langchainjs — semantic(0.35)+shared_fw(langchain)+same_lang+similar_pop+shared_platform (50%)

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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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