An official Qdrant MCP server implementation that provides semantic memory capabilities for AI agents. Enables storing and retrieving information using vector search, acting as a persistent knowledge layer on top of the Qdrant vector database.
Scanned 6/2/2026
Install to Claude Code
npx -y skills add agentskillexchange/skills --skill qdrant-mcp-server-vector-search-semantic-memory --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: "Qdrant MCP Server for Vector Search and Semantic Memory"
slug: "qdrant-mcp-server-vector-search-semantic-memory"
description: "An official Qdrant MCP server implementation that provides semantic memory capabilities for AI agents. Enables storing and retrieving information using vector search, acting as a persistent knowledge layer on top of the Qdrant vector database."
github_stars: 1314
verification: "security_reviewed"
source: "https://github.com/qdrant/mcp-server-qdrant"
category: "Integrations & Connectors"
framework: "MCP"
tool_ecosystem:
github_repo: "qdrant/mcp-server-qdrant"
github_stars: 1314
---
# Qdrant MCP Server for Vector Search and Semantic Memory
An official Qdrant MCP server implementation that provides semantic memory capabilities for AI agents. Enables storing and retrieving information using vector search, acting as a persistent knowledge layer on top of the Qdrant vector database.
## Installation
Use the upstream install or setup path that matches your environment:
- docker build -t mcp-server-qdrant .
- docker run -p 8000:8000 \
- npx @smithery/cli install mcp-server-qdrant --client claude
- [](https://insiders.vscode.dev/redirect/mcp/install?name=qdrant&config=%7B%2...
Requirements and caveats from upstream:
- ### Using Docker
- necessary when running the server in a Docker container.
- The value of 'metadata' is a Python dictionary with strings as keys. \
Basic usage or getting-started notes:
- This repository is an example of how to create a MCP server for [Qdrant](https://qdrant.tech/), a vector search engine.
- | FASTMCP_SERVER_PORT | Port to run the server on | 8000 |
- ### Using uvx
- Source: https://github.com/qdrant/mcp-server-qdrant
- Extracted from upstream docs: https://raw.githubusercontent.com/qdrant/mcp-server-qdrant/HEAD/README.md
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/qdrant-mcp-server-vector-search-semantic-memory/)
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