What are the system requirements?
Python 3.10 or higher and PostgreSQL 14 or higher with the pgvector extension are required.
MCP RAG Server
@karaage0703
Basic information
About MCP RAG Server
No overview available yet
MCP RAG Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-rag-server": {
"command": "uv",
"args": [
"sync"
]
}
}
}MCP RAG Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP RAG Server?
MCP RAG Server is a Python server that provides Retrieval-Augmented Generation (RAG) functionality compliant with the Model Context Protocol (MCP). It indexes documents in formats such as Markdown, text, PowerPoint, Word, and PDF using the multilingual-e5-large embedding model, and retrieves relevant information via vector search with PostgreSQL and pgvector. It is designed for developers building MCP-based RAG applications.
How to use MCP RAG Server?
Install dependencies with uv sync, set up PostgreSQL 14+ with the pgvector extension, and configure environment variables in .env. Start the MCP server with uv run python -m src.main or use CLI commands like python -m src.cli index to index documents. MCP hosts (e.g., Claude Desktop, Cline, Cursor) can integrate the server via a JSON configuration entry specifying command and args.
Key features of MCP RAG Server
- Supports multiple document formats (Markdown, text, PowerPoint, Word, PDF)
- Embedding model configurable (default: multilingual-e5-large)
- Vector search with PostgreSQL and pgvector
- Differential indexing (processes only new or modified files)
- Context chunk and full document retrieval
- CLI tools for index management and document count
Use cases of MCP RAG Server
- Searching document repositories with natural language queries
- Building MCP-compatible RAG applications
- Indexing and querying slides, PDFs, and text documents
- Incrementally updating a document index without reprocessing unchanged files
Frequently asked questions
How do I change the embedding model?
Set the `EMBEDDING_MODEL`, `EMBEDDING_DIM`, and prefix environment variables in `.env`, then clear and reindex using `python -m src.cli clear` and `python -m src.cli index`.
How can I backup and restore the indexed data?
Backup the PostgreSQL database with `pg_dump` and optionally the processed document directory. To restore, set up a new PostgreSQL instance with pgvector, create the database, restore the dump, and copy processed files if needed.
What transport does the server use?
The server uses JSON-RPC over stdio.
How is authentication handled?
Authentication is not mentioned in the README.
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