RAG MCP Adapter

A small, read-only Model Context Protocol (MCP) adapter for an existing RAG API.

It exposes the RAG system as standard MCP tools so MCP-compatible clients and agents can:

• check the health of the RAG service

• ask questions against indexed documents

• optionally restrict retrieval to a specific file

• receive answers, sources, evidence scores, and model metadata

Architecture

MCP Client
    |
    v
rag_mcp_server.py
    |
    | HTTP
    v
RAG API: http://localhost:8003
    |
    v
Qdrant + Elasticsearch + reranker + local vLLM

The adapter does not contain the RAG pipeline itself. It calls the existing RAG API through the RAG_API_URL environment variable.

Related MCP server: consulting-mcp-server

MCP Tools

get_rag_health()

Checks whether the RAG API, Qdrant, and Elasticsearch are available.

ask_documents(...)

Asks a question against the indexed document collection.

Main inputs:

• question: required question text

• filename: optional file filter

• limit: maximum retrieval candidates

• min_score: retrieval threshold

• evidence_min_score: minimum evidence threshold

• max_new_tokens: answer generation limit

The result includes the answer, sources, evidence score, generator model, and orchestrator.

Project Files

rag-mcp-adapter/
├── rag_mcp_server.py
├── test_mcp_client.py
├── test_mcp_client_basic.py
├── requirements.txt
├── .env.example
└── README.md

Setup

cd ~/Downloads/rag-mcp-adapter

python3 -m venv .venv
source .venv/bin/activate

python -m pip install --upgrade pip
python -m pip install -r requirements.txt

Set the RAG API address:

export RAG_API_URL=http://localhost:8003

Running the Complete System

Terminal 1 — start vLLM

cd ~/vllm-qwen
source .venv/bin/activate

export VLLM_USE_FLASHINFER_SAMPLER=0

vllm serve "$HOME/models/Qwen2.5-7B-Instruct-AWQ" \
  --served-model-name qwen-local \
  --host 0.0.0.0 \
  --port 8001 \
  --max-model-len 10000 \
  --gpu-memory-utilization 0.88

Leave this terminal running.

Terminal 2 — start the RAG API

cd ~/Downloads/rag-solution
docker compose up -d

export RAG_API_URL=http://localhost:8003

curl -s "$RAG_API_URL/health" | python3 -m json.tool

Terminal 3 — run the MCP tests

cd ~/Downloads/rag-mcp-adapter
source .venv/bin/activate

export RAG_API_URL=http://localhost:8003

python test_mcp_client.py

The expanded suite currently checks:

• MCP connection and tool discovery

• generated tool schema

• RAG service health

• filtered DOCX retrieval

• filtered TXT retrieval

• full-corpus retrieval

• unsupported-question refusal

• additional document facts

• response metadata

• source provenance

• empty-input validation

Expected summary:

Passed: 12/12
Failed: 0/12

A JSON report is written to:

mcp_test_report.json

Notes

• The adapter is intentionally read-only.

• The RAG API must be running at http://localhost:8003.

• The local vLLM/Qwen server must be running at http://localhost:8001/v1.

• Important: the RAG /health endpoint can still report that Qdrant and Elasticsearch are healthy when vLLM is offline. In that case, MCP discovery and health checks may pass, but supported ask_documents requests can return HTTP 500 Internal Server Error.

• With both services running, the complete integration test should report 12/12 tests passed.

• The adapter can be moved or deployed separately because it only depends on the RAG API URL.

• Do not expose destructive tools such as deleting documents or clearing indexes unless there is a clear requirement and appropriate access control.