py-mcp-qdrant-rag

Item: py-mcp-qdrant-rag
Rating: 69
Author: M8ven

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

→ amornpan/py-mcp-qdrant-rag · listed on glama

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// key findings

✅

No credential exfiltration, no sensitive file access, no obfuscation

Static analysis found nothing flowing your secrets to unexpected places.

🔐

You'll be asked for 1 credential: OPENAI_API_KEY

These are read from process.env at runtime. Make sure you trust where they’ll be sent.

// required environment variables

This server reads these from process.env. You'll be asked to provide them before it can run.

configQDRANT_URL— "": "http://localhost:6333",

configEMBEDDING_PROVIDER— "": "ollama",

configEMBEDDING_MODEL— Model name for embeddings nomic-embed-text (Ollama) or text-embedding-3-small (OpenAI) No

🔐 secretOPENAI_API_KEY— OpenAI API key (if using OpenAI) - If using OpenAI

configPORT

configOLLAMA_URL— "": "http://localhost:11434"

configQDRANT_COLLECTION

// full audit trail

The full breakdown of what we checked, the deductions that landed, the network hosts, the dependency advisories, and concrete fix guidance is available to verified publishers.

// improvement guidance — verified publishers only

We have 5 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.

// embed badge in your README

[![M8ven Score](https://m8ven.ai/badge/mcp/amornpan-py-mcp-qdrant-rag-7eltvp)](https://m8ven.ai/mcp/amornpan-py-mcp-qdrant-rag-7eltvp)

commit: 16f8059fa3c3b5a80371063fb6c90648effaf335

code hash: cf98207654f3ba91a5e369e51631757008bbf1176fb4d1a9c91e1e8a6846ea4f

verified: 6/17/2026, 12:29:14 PM

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