Qdrant 向量搜索 MCP 是 AI Skill Hub 本期精选MCP工具之一。已获得 1.4k 颗 GitHub Star,综合评分 8.5 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
Qdrant 向量搜索 MCP 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
Qdrant 向量搜索 MCP 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/qdrant/mcp-server-qdrant
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"qdrant------mcp": {
"command": "npx",
"args": ["-y", "mcp-server-qdrant"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Qdrant 向量搜索 MCP 执行以下任务... Claude: [自动调用 Qdrant 向量搜索 MCP MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"qdrant______mcp": {
"command": "npx",
"args": ["-y", "mcp-server-qdrant"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database.
A Dockerfile is available for building and running the MCP server:
```bash
docker build -t mcp-server-qdrant .
To install Qdrant MCP Server for Claude Desktop automatically via Smithery:
npx @smithery/cli install mcp-server-qdrant --client claude
Configuration is done via environment variables. The only command-line argument is --transport, used to select the transport protocol.
[!NOTE] You cannot provide bothQDRANT_URLandQDRANT_LOCAL_PATHat the same time.
| Name | Description | Default Value |
|---|---|---|
QDRANT_URL | URL of the Qdrant server | None |
QDRANT_API_KEY | API key for the Qdrant server | None |
COLLECTION_NAME | Name of the default collection to use. | None |
QDRANT_LOCAL_PATH | Path to the local Qdrant database (alternative to QDRANT_URL) | None |
EMBEDDING_PROVIDER | Embedding provider to use (currently only "fastembed" is supported) | fastembed |
EMBEDDING_MODEL | Name of the embedding model to use | sentence-transformers/all-MiniLM-L6-v2 |
TOOL_STORE_DESCRIPTION | Custom description for the store tool | See default in [settings.py](src/mcp_server_qdrant/settings.py) |
TOOL_FIND_DESCRIPTION | Custom description for the find tool | See default in [settings.py](src/mcp_server_qdrant/settings.py) |
QDRANT_SEARCH_LIMIT | Maximum number of results to return from search | 10 |
QDRANT_READ_ONLY | Enable read-only mode (disables qdrant-store tool) | false |
Since mcp-server-qdrant is based on FastMCP, it also supports all the FastMCP environment variables. The most important ones are listed below:
| Environment Variable | Description | Default Value |
|---|---|---|
FASTMCP_LOG_LEVEL | Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | INFO |
FASTMCP_SERVER_DEBUG | Enable debug mode | false |
FASTMCP_SERVER_HOST | Host address to bind the server to | 127.0.0.1 |
FASTMCP_SERVER_PORT | Port to run the server on | 8000 |
FASTMCP_SERVER_ON_DUPLICATE_RESOURCES | Behavior for duplicate resources (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_ON_DUPLICATE_TOOLS | Behavior for duplicate tools (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_ON_DUPLICATE_PROMPTS | Behavior for duplicate prompts (warn, error, replace, ignore) | warn |
FASTMCP_SERVER_DEPENDENCIES | List of dependencies to install in the server environment | [] |
[!NOTE] Server-specific settings use the FASTMCP_SERVER_ prefix. This may change in future versions.
To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:
{
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
"QDRANT_API_KEY": "your_api_key",
"COLLECTION_NAME": "your-collection-name",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
For local Qdrant mode:
{
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
"COLLECTION_NAME": "your-collection-name",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
This MCP server will automatically create a collection with the specified name if it doesn't exist.
By default, the server will use the sentence-transformers/all-MiniLM-L6-v2 embedding model to encode memories. For the time being, only FastEmbed models are supported.
For one-click installation, click one of the install buttons below:
Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
}
Or if you prefer using Docker, add this configuration instead:
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "docker",
"args": [
"run",
"-p", "8000:8000",
"-i",
"--rm",
"-e", "QDRANT_URL",
"-e", "QDRANT_API_KEY",
"-e", "COLLECTION_NAME",
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
}
Alternatively, you can create a .vscode/mcp.json file in your workspace with the following content:
{
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
For workspace configuration with Docker, use this in .vscode/mcp.json:
{
"inputs": [
{
"type": "promptString",
"id": "qdrantUrl",
"description": "Qdrant URL"
},
{
"type": "promptString",
"id": "qdrantApiKey",
"description": "Qdrant API Key",
"password": true
},
{
"type": "promptString",
"id": "collectionName",
"description": "Collection Name"
}
],
"servers": {
"qdrant": {
"command": "docker",
"args": [
"run",
"-p", "8000:8000",
"-i",
"--rm",
"-e", "QDRANT_URL",
"-e", "QDRANT_API_KEY",
"-e", "COLLECTION_NAME",
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "${input:qdrantUrl}",
"QDRANT_API_KEY": "${input:qdrantApiKey}",
"COLLECTION_NAME": "${input:collectionName}"
}
}
}
}
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
经综合评估,Qdrant 向量搜索 MCP 在MCP工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | mcp-server-qdrant |
| 原始描述 | An official Qdrant Model Context Protocol (MCP) server implementation |
| Topics | claudecursorllmmcpmcp-serversemantic-search |
| GitHub | https://github.com/qdrant/mcp-server-qdrant |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-05-22 · 更新时间:2026-05-22 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端