A Model Context Protocol (MCP) server for semantic search and Retrieval-Augmented Generation (RAG) over local codebases and documents.
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":"source-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.
<div align="center"> <h1>🔍 Source-MCP</h1> <p><strong>A Model Context Protocol (MCP) server for semantic search and Retrieval-Augmented Generation (RAG) over local codebases and documents.</strong></p> </div> --- ## 📖 Overview **Source-MCP** leverages the [Model Context Protocol](https://modelcontextprotocol.io) to provide AI assistants (like Claude, Gemini, and others) with direct access to local files through semantic search. Instead of manually copy-pasting code or documentation into your prompts, Source-MCP automatically indexes your local repository, generates vector embeddings, and enables the AI to semantically search and retrieve only the most relevant files. ## ✨ Key Features - **Dual Embedding Support:** - **OpenAI:** Uses robust `text-embedding-3-small` (1536 dimensions) for high-quality enterprise embeddings. - **FastEmbed (Local):** Uses `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` (384 dims). Runs entirely locally, no API keys required, and supports multilingual inquiries. - **Smart Incremental Indexing:** Uses file fingerprints (modified time + size) to only index new or modified files, ensuring lightning-fast startup times. - **Auto-Migration:** Automatically detects embedding dimension changes (e.g., switching from OpenAI to FastEmbed) and safely recreates the vector index. - **Web Dashboard (Port 8000):** - **Live Logs:** View real-time indexing and search activity with auto-scroll. - **Reindex Base:** Force-wipe the vector DB and manifest for a completely fresh full scan. - **Reindex Base:** Force-wipe the vector DB and manifest for a completely fresh full scan. - **Search Debugging:** Special endpoint (`/api/search/debug?q=...`) to test raw semantic search scores. ## 🤔 Why local embeddings and `zvec`? We use [**zvec**](https://github.com/alibaba/zvec), a lightweight, high-performance vector database maintained by Alibaba. `zvec` is embedded directly into the Python process, eliminating the need to set …
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.