context7-remote

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GitHub Repo

Overview

The context7-remote MCP Server is a hosted Model Context Protocol (MCP) server that provides AI assistants with on-demand access to up-to-date library and framework documentation via Context7. Instead of relying on potentially stale training data, AI workflows can retrieve current, version-specific docs and examples for popular programming languages, frameworks, and tools — directly inside an MCP-compatible assistant.

This server is especially useful for coding, debugging, and learning workflows where accuracy against the latest APIs and best practices matters.

Transport

streamable-http

Tools

  • resolve-library-id
  • get-library-docs

Key Capabilities

  • Live documentation access — Retrieve current docs rather than relying on model training cutoffs.
  • Library and framework coverage — Support for a wide range of popular languages, frameworks, and ecosystems.
  • Version-aware context — Return documentation aligned with modern or specified versions where available.
  • Reduced hallucination risk — Ground code suggestions and explanations in real, maintained sources.
  • Developer productivity — Enable assistants to answer “how do I use this API?” with accurate references.

How It Works

The context7-remote MCP Server is hosted and managed remotely, exposing an MCP endpoint that AI clients can connect to using standard MCP HTTP/SSE transport. When an agent invokes a tool the server queries Context7’s documentation index to locate and return the most relevant, up-to-date content.

The retrieved documentation is returned in a structured, AI-friendly format that can be summarized, cited, or used directly in code generation and explanation workflows. Because the server is remote, users don’t need to manage local processes or keep documentation sources in sync — updates are handled centrally.

This design makes modern documentation a first-class capability inside AI assistants, enabling workflows like “show me the latest way to configure this framework,” “what changed in this API?”, or “give me an example using the current syntax” — all without leaving the AI environment.