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M MCP Servers Developer low

upstash/context7

by Various

Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors

MCP

upstash/context7

Added 1 June 2026

#llm #mcp #mcp-server #vibe-coding

Overview

Context7 is a platform that generates and maintains up-to-date code documentation specifically formatted for LLM consumption and AI code editors. It indexes codebases and produces documentation that AI models can efficiently parse to provide accurate code suggestions and context.

Best for

Best for
Teams using AI code editors who need their LLMs to reference accurate, current codebase documentation without hallucination.

Use cases

  • Feed current codebase context to LLM-powered code completion tools
  • Keep AI code editors synchronized with live repository changes
  • Generate documentation optimized for model token efficiency

How to use

Install

npx ctx7 setup

Tested with

Claude Code, Cursor, Cline, VS Code

Notes

Context7 is a platform that generates and maintains up-to-date code documentation specifically formatted for LLM consumption and AI code editors. It indexes codebases and produces documentation that AI models can efficiently parse to provide accurate code suggestions and context.

56,548 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

  • Feed current codebase context to LLM-powered code completion tools
  • Keep AI code editors synchronized with live repository changes
  • Generate documentation optimized for model token efficiency

Pros

  • Designed specifically for LLM input rather than human readers, reducing token waste
  • Maintains documentation sync with active code changes automatically
  • Works with AI code editors and completion systems out of the box

Cons

  • Requires integration into existing development workflows and CI/CD pipelines
  • Documentation quality depends on codebase structure and clarity
  • Limited to TypeScript ecosystem based on current implementation

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Designed specifically for LLM input rather than human readers, reducing token waste
  • Maintains documentation sync with active code changes automatically
  • Works with AI code editors and completion systems out of the box

Cons

  • Requires integration into existing development workflows and CI/CD pipelines
  • Documentation quality depends on codebase structure and clarity
  • Limited to TypeScript ecosystem based on current implementation

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