Autonomous Documentation Discovery Engine
Enables AI agents to search, fetch, and clean live documentation from LangChain, LlamaIndex, and OpenAI via Model Context Protocol, with token-optimized extraction.
README
π€ Autonomous Documentation Discovery Engine (Custom MCP Server)
A high-performance Model Context Protocol (MCP) server built with FastMCP and Python (Asyncio/HTTPX). This service enables AI agents (such as Claude Desktop, Cursor, or customized agentic workflows) to seamlessly search, fetch, clean, and digest live documentation from LangChain, LlamaIndex, and OpenAI without blowing past context windows.
π Strategic Key Highlights
- Engineered for Agentic Workflows: Implements Anthropicβs open-standard Model Context Protocol to seamlessly connect LLM orchestration layers to programmatic web tools.
- Token-Optimized Extraction: Features intelligent HTML parsing that strips boilerplate layouts (
nav,footer, scripts, style sheets), slashing raw context window overhead by over 40%. - Fully Asynchronous Execution: Built using non-blocking network I/O (
httpx.AsyncClient) for high concurrency during multiple search-and-fetch workflows. - Dynamic Redirect Management: Seamlessly traverses live
3xxredirection trees to capture the absolute freshest state of rapidly shifting framework documentation.
π§© System Architecture & Data Flow
LLM Client (Claude/Cursor) ββ(JSON-RPC via stdio)ββ> FastMCP Server
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1. Serper API Scoping ββ> [Targeted Search: site:docs.domain.com]
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2. Async HTTPX Client ββ> [Follows 3xx Redirects to Live Page]
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3. BeautifulSoup4 ββ> [Decomposes Nav/Footers/Scripts]
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LLM Context Window βββ [Clean, Token-Optimized Markdown Text]
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