MCP Documentation Search Server

The MCP Documentation Search Server is a FastMCP‑based service that gives AI assistants instant, unified access to the documentation of several widely used JavaScript and TypeScript libraries. By exposing a single, well‑defined API, the server removes the need for each assistant to implement bespoke scraping or search logic for every framework it encounters. This solves a common pain point in AI development: the fragmentation of documentation sources and the overhead of maintaining multiple integration points.

At its core, the server accepts a query string and an optional library identifier, then orchestrates a DuckDuckGo‑powered search against the target site. It normalizes library names—handling aliases like “framer”, “framermotion”, or “motion”—and resolves the correct documentation URL before fetching and parsing content. The result is a concise, context‑rich snippet that the assistant can embed in responses or use to drive further reasoning. The asynchronous architecture and parallel request handling ensure that latency stays low even when querying multiple sites simultaneously.

Key capabilities include: multi‑library support for LangChain, LangGraph.js, Next.js, Tailwind CSS, FastMCP itself, and Framer Motion; intelligent name resolution that tolerates user typos or shorthand; robust error handling covering network timeouts, HTTP errors, and invalid inputs; and a clean test suite that guarantees reliability across unit, integration, and asynchronous scenarios. These features make the server highly dependable for production workloads where uptime and correctness are critical.

Real‑world use cases abound. A developer chatbot can answer “How do I implement an app router in Next.js?” by pulling the exact documentation paragraph, reducing hallucination risk. A code‑generation assistant can retrieve Tailwind CSS utility references on the fly, ensuring generated styles are accurate. Teams building internal tooling can embed the server into their knowledge bases, allowing agents to surface up‑to‑date library docs without manual updates.

Integration into AI workflows is straightforward: the server exposes a single MCP endpoint that accepts JSON payloads with and . Agents built on Claude, GPT‑4o, or any MCP‑compatible model can call this endpoint, receive structured results, and incorporate them into their replies. Because the server handles all search logic, developers can focus on higher‑level reasoning or UI design rather than low‑level web scraping.

What sets this MCP server apart is its blend of breadth and precision. By covering multiple popular frameworks under one roof, it eliminates the need for separate tools per library. Its intelligent search logic and error resilience provide trustworthy outputs, while its asynchronous design keeps response times minimal—critical for conversational AI that must maintain a fluid user experience.