Knowledge Base MCP Server (Qdrant)
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Knowledge Base MCP Server (Qdrant)
Read-only MCP server with hybrid search — dense semantic (all-MiniLM-L6-v2) + sparse keyword (BM-25) — fused with Qdrant's built-in RRF.
Stack
Layer | Tool | Cost |
MCP framework | FastMCP | Free |
Vector DB | Qdrant Cloud | Free (1 GB) |
Embeddings | FastEmbed (local, ONNX) | Free |
Hosting | Render | Free tier |
ChatGPT | Deep Research connector | Free (Pro plan) |
Related MCP server: mcp-documentacao
Setup
1. Install dependencies
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt2. Set up Qdrant Cloud
Go to https://cloud.qdrant.io → create a free cluster
Copy the cluster URL and API key from the Access tab
The collection is created automatically on first ingest
3. Configure .env
cp .env.example .env
# fill in QDRANT_URL, QDRANT_API_KEY, MCP_API_KEY4. Upload documents
python ingest.py --file report.pdf --dept 1 --pos 2
python ingest.py --file notes.md
python ingest.py --list
python ingest.py --delete <document_id>Supported formats: pdf, docx, md, txt, rst
5. Test locally
python server.py
# → http://localhost:8000/mcp6. Deploy to Render
Push to GitHub (
.envis gitignored)Render → New Web Service → Connect GitHub repo
Add env vars:
QDRANT_URL,QDRANT_API_KEY,KEYCLOAK_REALM_URL,KEYCLOAK_CLIENT_IDCopy your public URL
7. Connect to ChatGPT
Settings → Connectors → Add → paste Render URL +
/mcp/Auth: Bearer token → your
MCP_API_KEYUse via
+→ Deep Research → select your connector
Tools (all read-only)
Tool | Description |
| Hybrid search. Returns point IDs. |
| Get chunk content by point ID. |
| List all documents. |
| Full text of a document by document_id. |
search + fetch follow the ChatGPT Deep Research contract.
Tool Schema Changelog
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This server cannot be deployed
Maintenance
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