LlamaIndex Docs MCP lets AI agents search and navigate LlamaIndex documentation using semantic search, exact text and regex matching, documentation browsing, and direct page reading for accurate technical guidance.
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 LlamaIndex Docs MCP to ChatGPT through MewCP, so ChatGPT can use LlamaIndex Docs MCP's 4 tools, like search_docs, grep_docs and read_doc.
LlamaIndex Docs MCP needs no credentials, but every call still runs through MewCP: scoped, checked and logged like any other.
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Free to start.
Scope access to the exact LlamaIndex Docs 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.
Search through llama-index documentation using BM25 ranked search. Best for conceptual queries, finding documentation by topic, or when you're not sure of exact terminology. Returns ranked results with excerpts showing match context. Use this when: exploring unfamiliar topics, looking for 'how to' guides, searching by concept rather than exact terms, or need to understand what documentation exists on a subject. For exact term matching or code patterns, use grep_docs instead.
Search for exact text or regex patterns across documentation files. Returns all matching lines with their file locations and optional surrounding context. Best for: finding specific class/function names, locating exact error messages or code snippets, discovering all occurrences of a term, or pattern matching with regex. Unlike search_docs which ranks by relevance, this returns ALL matches in order. Use when you know precise terminology or need exhaustive results. Can return up to 500 matches vs search_docs' 20 result limit.
Read content from a specific documentation file by its path. By default returns the first 500 lines to avoid overwhelming context. Use startLine/endLine to read specific portions of longer documents. Accepts URL-style paths (e.g., '/python/framework/query-engine') or paths without extensions. Best for: reading documentation pages, understanding context after finding relevant excerpts, or accessing files you already know the path to.
Browse the documentation site structure to discover what pages and sections exist. Returns a tree showing directory slugs (usable as read_doc paths) and labels. Default depth is 1 (compact overview) when no section is specified, or 2 when a section/path is specified. Use depth=2-3 to see more detail. The path parameter performs a strict lookup — on a miss no tree is returned, just a short not-found message listing the searched sections; call again without path to browse.
