Recursively scrapes websites and builds a localized memory context to return specific reference text chunks.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"ragify-docs-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# 🔌 RAGify Docs: Recursive Documentation Scraper MCP Server A production-ready Model Context Protocol (MCP) server that empowers AI agents to recursively scrape entire software documentation sites, compile them into an in-memory vector store, and provide accurate, context-grounded answers to developer questions. Built using the **FastMCP Framework**, **LangChain**, and **Ollama**, this server lets tools like Claude Desktop, Cursor, or Zed read documentation pages in real-time to resolve coding problems without leaving the chat interface. --- ## 🔥 Key Features - **Recursive Deep Scraping:** Crawls documentation sites up to two levels deep out of the box using custom beautifulsoup extraction. - **Dynamic Context Assembly:** Automatically splits raw website texts into clean, code-aware semantic blocks. - **High-Diversity MMR Search:** Uses Maximal Marginal Relevance to fetch contrasting context points rather than duplicating search matches from single sections. - **Local-First Architecture:** Leverages a lightweight in-memory vector index alongside local `llama3.2` models for privacy and cost efficiency. - **Safe Data Pipeline:** Explicitly channels all background terminal metrics into `stderr` to avoid protocol communication corruption over standard I/O channels. --- ## 🛠️ Prerequisites Before installing the server, ensure your local environment contains the following applications: - **Python:** version 3.10 or higher. - **Ollama:** Installed and running locally with the target model pulled: ```bash ollama pull llama3.2 ``` --- ## 📦 Installation & Setup Follow these steps to download and configure the project directory on your local machine. ### 1. Clone and Navigate to the Project ```bash git clone https://github.com cd ragify_docs_2.0 ``` ### 2. Set Up a Virtual Environment Create and boot up an isolated Python runtime container to avoid package conflicts: ```powershell # Windows PowerShell p…
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