Context 7
AIOpen SourceFreeMCP server that transforms code documentation into up-to-date context, code snippets, and embeddings for LLMs and AI code editors.
-(0 Reviews)
Free Available
Starting from Free
Context 7
AIOpen SourceFree
MCP server that transforms code documentation into up-to-date context, code snippets, and embeddings for LLMs and AI code editors.
-(0 Reviews)
Free Available
Starting from Free
About Context 7
Context7 is an MCP (Model Context Protocol) server and pipeline that converts documentation repositories into actionable, retrievable context for LLMs, AI agents, and developer tooling. It parses common documentation formats, uses LLMs to extract targeted code snippets and metadata, generates vector embeddings, and exposes fast contextual retrieval via HTTP/MCP APIs and a web interface. Context7 is designed for easy integration with editors and agent runtimes (VS Code, LM Studio, Claude Desktop, etc.), enabling up-to-date, snippet-level documentation injection into AI workflows and code assistants.
Key Features
Document Format Support: Parses multiple documentation formats (.md, .mdx, .txt, .rst, .ipynb) to ingest source content from repositories and docs sites.
LLM-Powered Extraction: Uses LLMs to automatically extract high-quality, targeted code snippets and craft concise descriptive metadata for each snippet.
Embedding Generation Pipeline: Converts extracted snippets and metadata into vector embeddings for semantic search and fast similarity retrieval.
MCP Protocol Server: Implements the Model Context Protocol to serve context to editors and agent runtimes over HTTP/SSE and MCP endpoints.
Editor & Tooling Integrations: Provides configuration and one-click install patterns for popular editors and tools (VS Code, LM Studio, Claude Desktop, Amazon Q CLI) to deliver inline docs to code assistants.
API & Web Retrieval: Exposes web and API endpoints for instant contextual retrieval of relevant code examples and documentation snippets for LLMs and agents.
Deployment Options: Usable as a self-hosted server with Docker/CLI support and configurable mcp.json integration for diverse environments.
Auto-Updating Documentation: Designed to pull updates from documentation repositories so context served to models stays current with upstream docs.
Document parsing pipeline supporting .md, .mdx, .txt, .rst, .ipynb
LLM-powered context extraction to identify and summarize targeted code snippets with descriptive metadata
Embedding generation for snippets and metadata to enable vector-based retrieval
Contextual retrieval API via HTTP with support for streaming responses and legacy SSE endpoints
MCP protocol support and provider definition for editor/IDE integrations (e.g., VS Code, LM Studio)
NPM package distribution (@upstash/context7-mcp) and examples for npx-based invocation
Dockerfile and container-based deployment options
Configuration examples for Windows, Linux, and macOS, including one-click and manual MCP setups
Integration examples and tooling for agent platforms and third-party clients (Claude Desktop, Amazon Q Developer CLI)
Open-source repository with releases and community issue tracker
Use Cases
Augmenting Code Assistants: Provide up-to-date, snippet-level documentation to editor-integrated LLMs (VS Code, LM Studio) so code completions and explanations reference accurate examples.
Agent Context Libraries: Build and maintain searchable context libraries for autonomous agents that need fast access to relevant API usage examples and code snippets.
Retrieval-Augmented Generation: Serve precise code samples and metadata to LLMs at inference time to reduce hallucinations and improve code generation accuracy.
Private Repository Documentation Search: Ingest private docs/repos, generate embeddings, and enable semantic search across an organization's code docs for developer onboarding and support.
Tooling Integration for CI/CD: Integrate Context7 into developer workflows to surface documentation changes or examples during code review and continuous integration checks.
API Documentation Delivery: Transform API docs into structured, example-rich context to power chatbots, help centers, or interactive developer portals that answer coding questions with concrete examples.
Provide up-to-date, context-aware code examples and documentation snippets to LLM-powered coding assistants
Power IDE extensions (e.g., VS Code) to surface relevant library or API examples inline while coding
Serve as a backend for agents to quickly retrieve targeted documentation for tool use and reasoning
Build searchable documentation libraries with vector retrieval for customer support and developer docs
Integrate with agent frameworks and MCP-compatible clients to extend model context with external docs
MCP Server
https://github.com/upstash/context7💡 MCP (Model Context Protocol) enables AI assistants to securely interact with local and remote resources.
Screenshots
+
+
