Content
# MCP RAG Server
An independent Model Context Protocol (MCP) server that exposes a `search_documents` tool. This server connects to a PostgreSQL database (pgvector) to perform semantic retrieval. It fetches relevant document snippets and returns the raw context directly to the calling Agent/LLM (e.g., OpenWebUI), so the client's LLM can perform the final synthesis.
Built with Python, FastAPI, and the official MCP SDK.
## Project Structure
```
mcp-rag/
├── Dockerfile # Hardened Alpine-based Docker image
├── README.md
├── requirements.txt # Python dependencies
├── .env.example # Environment variables template
└── src/
├── config.py # Pydantic settings
├── rag.py # Similarity search and retrieval logic
└── server.py # FastAPI MCP server definition
```
## Setup & Configuration
1. Copy `.env.example` to `.env` and fill in your PostgreSQL connection details:
```bash
cp .env.example .env
```
2. Make sure your `POSTGRES_HOST` is accessible from the container.
## Running with Docker
This project provides a hardened Alpine-based Dockerfile.
1. Build the image:
```bash
docker build -t mcp-rag-server .
```
2. Run the container:
```bash
docker run -d \
--name mcp-rag \
--env-file .env \
-p 8000:8000 \
mcp-rag-server
```
The MCP Server will be accessible at `http://localhost:8000/mcp`. You can configure your MCP-compatible clients (like OpenWebUI) to connect via SSE to this endpoint.
## Available MCP Tools
- `search_documents(question: str, top_k: int = 5) -> str`
Executes a similarity search against the pgvector store and returns the raw text snippets and their citations. Does NOT perform LLM synthesis natively.
