Skip to content
Back to skills

Qdrant Mcp Server Vector Search Semantic Memory

ASecurity

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.

  • 36 stars
  • 0 votes
  • 0 copies
  • 3 views
  • Added June 2, 2026
ai-agentspythongodockergitdatabasesecurity

Works with

  • vscode
  • cli
  • mcp

Security analysis

A100/100

Scanned June 2, 2026

npx -y skills add agentskillexchange/skills --skill qdrant-mcp-server-vector-search-semantic-memory --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Qdrant Mcp Server Vector Search Semantic Memory?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Qdrant Mcp Server Vector Search Semantic Memory
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/agentskillexchange-qdrant-mcp-server-vector-search-semantic-memory/badge)](https://www.skillsdirectory.com/skills/agentskillexchange-qdrant-mcp-server-vector-search-semantic-memory)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
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
- [![Install with Docker in VS Code](https://img.shields.io/badge/VS_Code-Docker-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](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/)

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.