Official Context7 MCP server that brings up-to-date, version-specific library documentation and code examples into AI coding prompts.
ChatGPT is OpenAI's assistant. It can add custom MCP servers as plugins, which lets it call your tools from a conversation.
This guide connects Context7 MCP to ChatGPT through MewCP, so ChatGPT can use Context7 MCP's 2 tools, like resolve-library-id and query-docs, without your Context7 MCP credentials ever reaching the app.
Context7 MCP signs in through managed OAuth. MewCP stores the token encrypted, refreshes it before it expires and injects it only at call time. Your agent never holds it, so it can never leak it.
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Free to start.
Scope access to the exact Context7 MCP tools and accounts you allow, work and personal side by side. Anything outside that scope is refused at call time, even when the model asks for it.
Each call is logged: what ran, on which account, and whether it worked. Failures come back structured, so the agent knows whether to retry instead of guessing.
Every app sits behind one secure MCP URL, and there is no server for you to deploy or keep running. Add an integration in the dashboard and it is live on the next call, with no config to change and no new credentials to wire up. Tools load only when a task needs them, so your agent stays fast and focused however many apps you connect.
In ChatGPT, open Plugins and choose Add custom MCP.
Name it "mewcp" and paste the server URL.
Click Connect. A sign-in window opens; sign in to MewCP and approve access. No API key needed.
Start a new chat and ask: "List the tools you can use from MewCP." You should see your connected apps.
Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'Query Documentation' tool to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Each result includes: - Library ID: Context7-compatible identifier (format: /org/project) - Name: Library or package name - Description: Short summary - Code Snippets: Number of available code examples - Source Reputation: Authority indicator (High, Medium, Low, or Unknown) - Benchmark Score: Quality indicator (100 is the highest score) - Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version. For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.
Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework. You must call 'Resolve Context7 Library ID' tool first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Do not call this tool more than 3 times per question.
