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MCP-RAG: Modular RAG Pipeline using MCP & GroundX

@sujithadr

Repository

Basic information

Category

Memory & Knowledge

License

MIT license

Transports

stdio

Author

sujithadr

About MCP-RAG: Modular RAG Pipeline using MCP & GroundX

MCP server Implantation for RAG (GroundX API)

#mathgpt#math-solver#math-assistant

MCP-RAG: Modular RAG Pipeline using MCP & GroundX config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-rag-sujithadr": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-r",
        "pyproject.toml"
      ]
    }
  }
}

MCP-RAG: Modular RAG Pipeline using MCP & GroundX 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: Modular RAG Pipeline using MCP & GroundX?

MCP-RAG is a modular, production-grade Retrieval-Augmented Generation (RAG) system that integrates GroundX for semantic search and ingestion, OpenAI GPT-4 for response generation, and the Model Context Protocol (MCP) for standardized tool orchestration. It is designed for teams building AI-driven applications with reusable components.

How to use MCP-RAG: Modular RAG Pipeline using MCP & GroundX?

Set up a .env file with your OPENAI_API_KEY and GROUNDEX_API_KEY, then install dependencies with uv pip install -r pyproject.toml. Start the MCP server with mcp dev server.py. Ingest a PDF using mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}' and perform a search with mcp call process_search_query --args '{"query": "What is explained in section 3?"}'.

Key features of MCP-RAG: Modular RAG Pipeline using MCP & GroundX

  • Modular tool design using MCP server interface
  • YAML-based prompt templates with Jinja2 rendering
  • PDF file ingestion into GroundX vector store
  • Real-time semantic search via GroundX Search Tool
  • Plug-and-play API integration for new tools

Use cases of MCP-RAG: Modular RAG Pipeline using MCP & GroundX

  • Ingest PDF documents and perform semantic search on their content
  • Build a modular RAG pipeline with clean separation of concerns
  • Use standardized MCP tool orchestration for AI applications

Frequently asked questions

1

What dependencies are required?

Python >=3.12, an OpenAI API key, a GroundX API key, and the `uv` package manager.

2

How do I ingest a PDF?

Run `mcp call ingest_documents --args '{"file_path": "data/sample.pdf"}'`.

3

How do I perform a semantic search?

Run `mcp call process_search_query --args '{"query": "What is explained in section 3?"}'`.

4

What is the architectural flow?

User query → MCP server routes to Search Tool → Search Tool queries GroundX API → snippets are rendered via YAML prompt → OpenAI API generates final LLM response.

5

Where is the ingested data stored?

Data is stored in the GroundX vector store (via the ingestion tool).

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