63
/100
C
◉ Complete 5⁄5
py-mcp-qdrant-rag
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.
Ollama
OpenAI
Qdrant
Glama
Complete visibility
— 5/5 applicable dimensions scored
✓ Schema Quality
✓ Protocol
— Reliability
✓ Docs & Maintenance
✓ Security Hygiene
✓ Schema Interpretability
Schema Quality
78
25% weight
Protocol Compliance
33
20% weight
Reliability
—
20% weight
Docs & Maintenance
21
15% weight
Security Hygiene
80
20% weight
Schema Interpretability
94
15% weight
Score History
Category Trends
30-Day Uptime
30 days ago
Today
Static Analysis
| Metric | Score | Rating |
|---|---|---|
| Schema Completeness | 90 | Good |
| Description Quality | 60 | Fair |
| Documentation Coverage | 45 | Fair |
| Maintenance Pulse | 11 | Poor |
| Dependency Health | 30 | Poor |
| License Clarity | — | Poor |
| Version Hygiene | — | Poor |
Analyzed 4 weeks, 1 day ago
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