Qdrant Vector Database
MCP Server for Qdrant provides a vector database integration for storing and retrieving information using semantic search capabilities. Built with Python, it supports multiple embedding providers including FastEmbed, sentence-transformers, and lightweight alternatives optimized for Alpine Linux environments with minimal dependencies. The server offers two main tools: 'qdrant-store' for saving text with optional metadata and 'qdrant-find' for semantic searching of stored information. It can be deployed via Docker or run locally, making it ideal for AI assistants that need persistent memory storage with efficient retrieval based on meaning rather than exact keyword matching.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/jimmy974/mcp-server-qdrant
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve Qdrant Vector Database from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.