iflow-mcp_mat1312_datapizza-mcp-server

# DataPizza MCP Server 🍕 A Model Context Protocol (MCP) server that provides intelligent access to datapizza-ai documentation through vector similarity search and retrieval-augmented generation. ## Overview This MCP server enables AI assistants and applications to query the comprehensive datapizza-ai documentation using natural language queries. It indexes documentation from the datapizza-ai repository and provides contextual, relevant responses through a RAG (Retrieval-Augmented Generation) pipeline. ## Features - **Intelligent Documentation Search**: Natural language queries across datapizza-ai documentation - **Vector-Based Retrieval**: Uses OpenAI embeddings and Qdrant vector database for semantic search - **MCP Protocol Compliance**: Standard Model Context Protocol implementation for broad compatibility - **Automatic Indexing**: Downloads and indexes documentation from GitHub automatically - **Cloud-Ready**: Supports Qdrant Cloud for scalable vector storage - **Configurable**: Environment-based configuration for flexible deployment ## Architecture The server consists of four main components: - **MCP Server**: FastMCP-based server exposing the `query_datapizza` tool - **Indexer**: Downloads and processes datapizza-ai documentation into searchable chunks - **Retriever**: RAG engine for semantic search and response generation - **Configuration**: Environment-based settings management with validation ## Prerequisites - Python 3.10 or higher - OpenAI API key - Qdrant Cloud account and API key - Internet connection for documentation indexing ## Installation 1. Clone the repository: ```bash git clone https://github.com/datapizza-labs/mcp_server_datapizza.git cd datapizza-mcp-server ``` 2. Navigate to the package directory: ```bash cd datapizza-mcp-server ``` 3. Install the package with development dependencies: ```bash pip install -e ".[dev]" ``` ## Configuration Create a `.env` file in the `datapizza-mcp-server` directory with the following variable…