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ChrisGVE/workspace-qdrant-mcp

by Various

Project-scoped Qdrant MCP server for workspace collections with scratchbook functionality. Python port of claude-qdrant-mcp with FastEmbed and project-aware collection management.

C

MCP

ChrisGVE/workspace-qdrant-mcp

Added 1 June 2026

#ai-tools #asyncio #claude-code #claude-desktop #cli #document-management #embeddings #fastmcp

Overview

A project-scoped Qdrant MCP server that manages workspace collections with a scratchbook feature. It is a Python port of claude-qdrant-mcp, using FastEmbed for embeddings and providing project-aware collection management.

Best for

Best for
Developers needing a lightweight, project-scoped Qdrant MCP server for workspace-specific vector storage

Use cases

  • Manage vector collections scoped to specific projects or workspaces
  • Store and retrieve embeddings with a scratchbook for quick notes
  • Integrate Qdrant vector search into MCP-compatible tools

Notes

A project-scoped Qdrant MCP server that manages workspace collections with a scratchbook feature. It is a Python port of claude-qdrant-mcp, using FastEmbed for embeddings and providing project-aware collection management.

1 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Manage vector collections scoped to specific projects or workspaces
  • Store and retrieve embeddings with a scratchbook for quick notes
  • Integrate Qdrant vector search into MCP-compatible tools

Pros

  • Project-aware collection management keeps data organized
  • Scratchbook feature enables quick ad-hoc storage
  • Leverages FastEmbed for efficient embedding generation

Cons

  • Very early stage with only 1 star and limited community adoption
  • Python port may lag behind the original Rust-based claude-qdrant-mcp
  • Dependency on FastEmbed may limit embedding model choices

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Project-aware collection management keeps data organized
  • Scratchbook feature enables quick ad-hoc storage
  • Leverages FastEmbed for efficient embedding generation

Cons

  • Very early stage with only 1 star and limited community adoption
  • Python port may lag behind the original Rust-based claude-qdrant-mcp
  • Dependency on FastEmbed may limit embedding model choices