kb-rag-mcp

Standalone MCP server exposing agentic-RAG tools over a pgvector-backed knowledge base. Designed for public internet deployment (Render) with bearer-token auth; embeddings via Azure OpenAI; LLM/agent side lives in Azure AI Foundry (or any MCP-capable client).

Tools

The agentic loop lives in the client agent: search → check low_confidence → refine query / scope by category / fetch full article → answer with citations.

Setup

1. cp .env.example .env and fill in every value (see comments in that file).
2. Create the schema: paste scripts/schema.sql into the Neon SQL editor (or psql "$DATABASE_URL" -f scripts/schema.sql).
3. Ingest articles: python scripts/ingest.py scripts/data/kb_dummy_200.json
4. Run locally: uvicorn server:app --port 8080

Auth

Every /mcp request needs Authorization: Bearer <key> where <key> is listed in MCP_API_KEYS (comma-separated; one key per client, rotate by adding/removing). No keys configured = all requests rejected. /health is open (used by Render health checks).

Deploy (Render)

1. Push this directory to a GitHub repo.
2. Render → New → Blueprint → select the repo (render.yaml is picked up automatically).
3. Fill the sync: false env vars in the Render dashboard (same values as your local .env).
4. Your MCP endpoint: https://<service>.onrender.com/mcp

Free-tier note: Render spins the service down after idle; first request after idle takes ~30-60s. Fine for testing; use a paid instance for a client-facing demo.

Smoke test

curl https://<service>.onrender.com/health

curl -X POST https://<service>.onrender.com/mcp \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'