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

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

MCP RAG Server