mcp-server-rapid-rag

# mcp-server-rapid-rag MCP Server for **rapid-rag** - Local RAG with semantic search and LLM queries. Search your documents with AI, no cloud needed! Works with Ollama for local LLM inference. ## Installation ```bash pip install mcp-server-rapid-rag ``` ## Configuration Add to your Claude Desktop config (`~/.config/claude/claude_desktop_config.json`): ```json { "mcpServers": { "rapid-rag": { "command": "mcp-server-rapid-rag" } } } ``` Or with uvx: ```json { "mcpServers": { "rapid-rag": { "command": "uvx", "args": ["mcp-server-rapid-rag"] } } } ``` ## Tools ### `rag_add` Add files or directories to the RAG collection. Supports .txt, .md, .pdf. ``` "Add my docs folder to RAG: ~/Documents/notes" ``` ### `rag_add_text` Add raw text directly to the collection. ``` "Store this meeting notes in RAG: [text content]" ``` ### `rag_search` Semantic search - find the most relevant documents. ``` "Search my documents for: Python async patterns" ``` ### `rag_query` Full RAG pipeline - search documents and get an AI-generated answer. ``` "Based on my documents, how do I configure logging?" ``` ### `rag_info` Get collection statistics. ``` "Show me the RAG collection info" ``` ### `rag_list` List all available collections. ``` "List my RAG collections" ``` ### `rag_clear` Clear a collection (requires confirmation). ``` "Clear the 'old_project' RAG collection" ``` ## Example Usage Ask Claude: > "Add all the markdown files from ~/projects/docs to my RAG" Claude will: 1. Index all .md files in the directory 2. Split them into chunks with embeddings 3. Store them in ChromaDB locally Then ask: > "Based on my docs, how do I set up authentication?" Claude will: 1. Search the indexed documents 2. Pass relevant chunks to Ollama 3. Generate an answer with source citations ## Requirements - **rapid-rag**: Core RAG library with ChromaDB - **Ollama** (optional): For `rag_query` - local LLM inference ### Install Ollama …