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Context7

Up-to-date docs for AI code editors via MCP

Context7 is an MCP server developed by Upstash that injects up-to-date, version-specific documentation directly into AI code editors and coding assistants. By typing 'use context7' in prompts, developers get accurate library documentation instead of hallucinated or outdated API references. It pulls from official source documentation and serves it through the Model Context Protocol, solving the common problem of LLMs generating code with incorrect or nonexistent API calls.

About Context7

Context7 tackles one of the most persistent problems in AI-assisted coding: LLMs generating code that calls APIs which do not exist, use deprecated syntax, or reference the wrong library version. Built by Upstash (the team behind serverless Redis and Kafka services), Context7 works as an MCP server that pulls documentation directly from official sources and injects version-specific, accurate references into the coding assistant's context window. Instead of the model hallucinating a plausible-looking but incorrect function signature, it works from the actual documentation for the exact version the developer is using.

The workflow is deliberately simple: a developer adds 'use context7' to their prompt in any MCP-compatible editor — Claude, Cursor, Windsurf, Cline, or others — and the server automatically fetches relevant documentation for the libraries mentioned in the query. There is no manual configuration of documentation sources or version pinning required; Context7 resolves the appropriate version and retrieves the matching docs. This approach means the coding assistant's suggestions align with what the library actually exposes rather than what the training data vaguely remembers from a snapshot taken months or years ago.

Context7 offers free public documentation access and has gained rapid traction in the MCP ecosystem as developers realized how much time they waste debugging AI-generated code that looks correct but fails against the current API surface. It is particularly valuable when working with fast-moving frameworks like Next.js, SvelteKit, or Tailwind where breaking changes between versions are common and LLM training data lags behind the latest releases. The project is open source on GitHub under the Upstash organization and can be installed as a remote MCP server with a single configuration entry.

Pricing & Platform Specs

Pricing Summary

Model Context Protocol (MCP) server for real-time, version-specific library documentation by Upstash. Free tier provides $0/mo for 1,000 monthly API calls on public open-source documentation. Pro tier starts at $10/seat/mo for 5,000+ API calls and private codebase/docs indexing. Enterprise tier provides custom pricing for self-hosted deployments, SAML SSO, SOC-2 compliance, and dedicated SLAs.

full pricing breakdown →

Supported Platforms

MCP server, works with Claude, Cursor, Windsurf, and other MCP clients

Explore categories, tags & use cases

Categories

Real-time web search and retrieval via MCP

Exa MCP Server provides AI coding agents with real-time web search and content crawling capabilities through the Model Context Protocol. It leverages Exa's neural search API for semantic understanding of queries, returning clean, structured results with full page content extraction. Supports both remote hosted MCP endpoints and local client configurations.

Open Source

Instant MCP server for any GitHub repository

GitMCP is a free, open-source remote MCP server that transforms any GitHub repository or GitHub Pages site into an AI-accessible documentation hub. Just replace github.com with gitmcp.io in any repo URL to give AI assistants grounded context about that project — eliminating code hallucinations with zero configuration required.

Open Source

QMD

On-device hybrid search engine for your docs and notes

QMD is an on-device search engine built by Tobi Lütke (Shopify CEO) that indexes markdown notes, meeting transcripts, and documentation locally. It combines BM25 full-text search, vector semantic search, and LLM-powered re-ranking into a single hybrid pipeline. Ships with a built-in MCP server for seamless integration with Claude Code, Cursor, and other AI editors. All processing happens on your machine via node-llama-cpp with GGUF models — zero cloud dependency.

Open Source

Side-by-Side Comparisons

Context7 logo
Context7
vs
Serena logo
Serena

Context7 vs Serena: Documentation Grounding or Semantic Code Intelligence?

Context7 and Serena are popular coding-agent companions, but they supply different kinds of context. Context7 serves as the more practical daily standard when the goal is current, version-specific library guidance; Serena is the stronger specialist for symbol-aware navigation, refactoring, and editing inside a large codebase.

Context7Serena
Context7 logo
Context7
vs
GitHub logo
GitHub MCP Server

Context7 vs GitHub MCP Server: Documentation Grounding or Repository Operations?

Context7 and GitHub MCP Server answer different MCP questions for coding agents. Context7 supplies version-aware library documentation so an agent writes against the right API surface, while GitHub MCP Server gives the agent repository, issue, pull request, and workflow context from GitHub. Choose Context7 first when dependency accuracy is the bottleneck; choose GitHub MCP Server when the agent must operate inside a real repo workflow.

Context7 logo
Context7
vs
Firecrawl MCP Server logo
Firecrawl MCP Server

Context7 vs Firecrawl MCP Server: Docs Context or Live Web Extraction?

Context7 and Firecrawl MCP Server solve different freshness problems for AI coding agents. Context7 injects version-specific library documentation into prompts, while Firecrawl brings live web search, scraping, crawling, and extraction into MCP clients. Choose Context7 first when the task is reliable API usage inside code; choose Firecrawl when the agent needs current public-web data or structured page extraction.

Context7 logo
Context7
vs
GitMCP logo
GitMCP

Context7 vs GitMCP — MCP Documentation Context Servers for AI Coding Agents

Context7 by Upstash and GitMCP are both MCP servers that inject up-to-date documentation into AI coding agents, solving the stale training data problem that causes hallucinated API calls. Context7 provides curated, version-specific library documentation for popular frameworks with 51K+ stars. GitMCP transforms any GitHub repository into an instant documentation source with 7.8K+ stars and zero configuration.

Context7GitMCP

Community experience

Sources & verification

Sources checked
Content verified

Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.

FAQ

What is Context7?

Context7 is an MCP server developed by Upstash that injects up-to-date, version-specific documentation directly into AI code editors and coding assistants. By typing 'use context7' in prompts, developers get accurate library documentation instead of hallucinated or outdated API references. It pulls from official source documentation and serves it through the Model Context Protocol, solving the common problem of LLMs generating code with incorrect or nonexistent API calls.

Is Context7 free?

Context7 offers a free tier alongside paid plans. Model Context Protocol (MCP) server for real-time, version-specific library documentation by Upstash. Free tier provides $0/mo for 1,000 monthly API calls on public open-source documentation. Pro tier starts at $10/seat/mo for 5,000+ API calls and private codebase/docs indexing. Enterprise tier provides custom pricing for self-hosted deployments, SAML SSO, SOC-2 compliance, and dedicated SLAs.

Is Context7 open source?

Yes — Context7 is open source.

Is Context7 still maintained?

Yes — Context7 is active. Its listing was last verified on September 6, 2026.

What are the best Context7 alternatives?

The first editor-selected Context7 alternatives are Exa MCP Server, GitMCP, QMD.

How does Context7 score in our review?

The published editorial review lists Context7 at 88/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.