---
id: mcp-server-qdrant
version: "0.8.1"
license: Apache-2.0
license_treatment: permissive
maintenance: aging
---

# mcp-server-qdrant — MCP server for retrieving context from a Qdrant vector database

License: permissive · Maintenance: aging · Downloads: 1.3M/mo

## What it is and what it does

mcp-server-qdrant bridges LLM applications and Qdrant vector search by implementing the Model Context Protocol (MCP) standard. It runs as a standalone server that exposes two core tools: qdrant-store (to persist text and metadata into Qdrant collections) and qdrant-find (to retrieve semantically similar results via vector search). The server uses fastembed for embedding generation and can connect to either a remote Qdrant instance or a local database.

Configuration is entirely environment-variable driven, supporting multiple transport protocols (stdio, SSE, streamable-HTTP) and deployment modes including Docker and uvx. It integrates with Claude Desktop and other MCP clients, enabling AI applications to maintain and query a persistent semantic memory layer without direct database access.

Use it for:

- Integrate Claude Desktop with Qdrant to give AI assistants persistent semantic memory across conversations
- Build AI-powered applications that store and retrieve domain-specific context from vector embeddings
- Deploy a containerized MCP server to provide remote LLM clients with vector search capabilities
- Create custom AI workflows where multiple agents share a centralized semantic knowledge base in Qdrant

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Exposes Qdrant vector search as a Model Context Protocol server, allowing LLM applications to store and retrieve semantic memories from a vector database via standardized MCP tools.

Yes, if you need MCP-compatible vector search integration. The package is straightforward to deploy, has low install friction, carries a permissive license, and has no known vulnerabilities. The aging maintenance status (247 days since last release) is a minor caution but not a blocker for stable, read-write workloads. Verify active support if you plan to rely on ongoing updates.

## Install

pip install mcp-server-qdrant
uv add mcp-server-qdrant
poetry add mcp-server-qdrant

## Installing mcp-server-qdrant

Before you install: Low install friction with a pure-Python wheel and four runtime dependencies. Maintenance status is aging—last release was 247 days ago—but the package remains functional and supports current Python versions.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most deployment contexts.

Quickstart:

# Install
pip install mcp-server-qdrant

# Set environment variables
export QDRANT_URL="http://localhost:6333"
export COLLECTION_NAME="my-collection"
export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"

# Run the server
from mcp_server_qdrant import app
# Server exposes qdrant-store and qdrant-find tools via MCP

Requires a running Qdrant instance (remote via QDRANT_URL or local via QDRANT_LOCAL_PATH) and Python >=3.10. fastembed will download its embedding model on first use.

Verify before relying:

- Whether the package is actively maintained or in maintenance-only mode beyond the aging signal
- Performance characteristics when handling large vector collections or high query volume
- Compatibility with specific Qdrant server versions or deployment modes

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 1.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

qdrant vector search mcp server, semantic memory storage for llms, model context protocol qdrant, vector database integration llm, mcp tools qdrant retrieval, embedding storage and search, llm memory layer qdrant, vector-search, llm-integration, mcp-protocol

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