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MCP RAG Server

MCP RAG Server

by Sujith29k
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

MCP RAG

MCP server that owns the full RAG pipeline, plus a small web client for uploading PDFs and asking questions.

Layout

src/mcp_rag/
  server/     MCP tools: ingest, index, search, ask
  client/     FastAPI UI: upload files, ask questions
data/docs/    PDF corpus (owned by the MCP server)
data/faiss_index/   cached embeddings
PDFs uploaded in the client
        │
        ▼
MCP server (:8001/mcp)
  ingest → chunk → embed → FAISS
  retrieve → generate (OpenAI / LangGraph)
        │  Streamable HTTP
        ▼
Web client (:8000)
  upload / delete / ask

Related MCP server: ragi

Requirements

  • Python 3.10+ (3.12 recommended)

  • uv

  • An OpenAI API key

Setup

uv sync
copy .env.example .env   # or: cp .env.example .env

Put your OpenAI key in .env. Optional sample PDFs:

uv run python scripts/make_sample_pdfs.py

Run

Terminal 1 — MCP server (RAG):

uv run python -m mcp_rag.server

Terminal 2 — web client:

uv run python -m mcp_rag.client

Open http://127.0.0.1:8000. Upload PDFs, then ask questions.

On Windows you can also use scripts\run_server.bat and scripts\run_client.bat.

MCP tools

Tool

Purpose

ingest_document

Add a PDF (base64) and rebuild the index

delete_document

Remove a PDF and rebuild the index

ensure_index

Build or reuse the FAISS index

search_documents

Semantic search over chunks

ask_question

Retrieve + generate a grounded answer

list_documents

List PDFs in the corpus

index_status

Index readiness and chunk count

Client API

Method

Path

Purpose

GET

/api/health

Client + MCP status

GET

/api/documents

List PDFs

POST

/api/upload

Multipart PDF upload

DELETE

/api/documents/{name}

Remove a PDF

POST

/api/reindex?force=true

Rebuild FAISS index

POST

/api/ask

{"question":"..."} → answer + sources

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

Related MCP Connectors

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