mcp-poc

A small Model Context Protocol (MCP) server that exposes the retrieval step of the sibling rag-poc project as an MCP tool — "RAG over MCP."

MCP is a standard way to give an LLM client (Claude Code, Claude Desktop, …) access to tools that live outside the model. Here the pattern is deliberate: the server does retrieval only — it embeds your query, finds the most similar chunks in rag-poc's local vector store, and hands them back. The client's model does the generation, reading those chunks and writing a grounded, cited answer. The server never calls a chat model.

The tool

Tool	Signature	What it does
rag_search	(query: str, k: int = 4) -> list[dict]	Embeds query with the same local Ollama model that built the store, cosine-ranks the stored chunks, and returns the top k as {source, score, text} (most similar first).

The model calling it is expected to answer from the returned chunks and cite each source, or say it doesn't know if they don't contain the answer.

How it connects to rag-poc

This repo doesn't reimplement RAG — it imports rag-poc's rag package. The server puts the rag-poc folder on sys.path and reuses its vector store, query embedder, and input-sanitising hook. By default it expects rag-poc as a sibling folder (../rag-poc); point elsewhere with the RAG_POC_PATH environment variable. The store is read from RAG_POC_PATH/store.npz.

Prerequisites

• Ollama running at localhost:11434 with the nomic-embed-text model pulled (ollama pull nomic-embed-text). The query must be embedded by the same model that embedded the documents.
• rag-poc ingested so its store exists — in the rag-poc folder: python main.py ingest.

Setup

cd "C:\Coding Space\mcp-poc"
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt

Test it — two ways

1. MCP Inspector (no Claude needed):

mcp dev server.py

Open the printed URL, pick rag_search, enter a query (e.g. "What is retrieval-augmented generation?"), and inspect the returned chunks with their similarity scores.

2. From Claude Code. Register the server so rag_search appears in your session:

claude mcp add mcp-poc -- "C:\Coding Space\mcp-poc\.venv\Scripts\python.exe" "C:\Coding Space\mcp-poc\server.py"

If rag-poc is not a sibling of this repo, pass its location when registering:

claude mcp add mcp-poc --env RAG_POC_PATH="C:\path\to\rag-poc" -- "C:\Coding Space\mcp-poc\.venv\Scripts\python.exe" "C:\Coding Space\mcp-poc\server.py"

Then /mcp lists connected servers, and you can ask a question about your indexed docs — Claude will call rag_search, pull the relevant chunks, and answer from them. Remove it with claude mcp remove mcp-poc.

Next steps

• Add a rag_answer tool that runs rag-poc's full local pipeline (Ollama generation) to compare "the client model generates" vs "the local model generates."
• Expose the indexed documents as MCP resources, or add a prompt template for a standard "answer with citations" instruction.