b9f773e99c
* Switch to Typer to read the CLI parameters and options * Rely on environmental variables only * Fix tests * Update README
141 lines
5.1 KiB
Markdown
141 lines
5.1 KiB
Markdown
# mcp-server-qdrant: A Qdrant MCP server
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[](https://smithery.ai/protocol/mcp-server-qdrant)
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> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables
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> seamless integration between LLM applications and external data sources and tools. Whether you’re building an
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> AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to
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> connect LLMs with the context they need.
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This repository is an example of how to create a MCP server for [Qdrant](https://qdrant.tech/), a vector search engine.
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<a href="https://glama.ai/mcp/servers/9ejy5scw5i"><img width="380" height="200" src="https://glama.ai/mcp/servers/9ejy5scw5i/badge" alt="mcp-server-qdrant MCP server" /></a>
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## Overview
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A basic Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine.
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It acts as a semantic memory layer on top of the Qdrant database.
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## Components
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### Tools
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1. `qdrant-store-memory`
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- Store a memory in the Qdrant database
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- Input:
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- `information` (string): Memory to store
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- Returns: Confirmation message
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2. `qdrant-find-memories`
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- Retrieve a memory from the Qdrant database
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- Input:
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- `query` (string): Query to retrieve a memory
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- Returns: Memories stored in the Qdrant database as separate messages
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## Installation in Claude Desktop
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### Using mcp (recommended)
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When using [`mcp`](https://github.com/modelcontextprotocol/python-sdk) no specific installation is needed to directly run *mcp-server-qdrant*.
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```shell
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mcp install src/mcp_server_qdrant/server.py \
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-v QDRANT_URL="http://localhost:6333" \
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-v QDRANT_API_KEY="your_api_key" \
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-v COLLECTION_NAME="my_collection" \
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-v EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"
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```
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### Installing via Smithery
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To install Qdrant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/protocol/mcp-server-qdrant):
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```bash
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npx @smithery/cli install mcp-server-qdrant --client claude
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```
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### Manual configuration
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To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your
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`claude_desktop_config.json`:
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```json
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{
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"qdrant": {
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"command": "uvx",
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"args": ["mcp-server-qdrant"],
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"env": {
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"QDRANT_URL": "http://localhost:6333",
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"QDRANT_API_KEY": "your_api_key",
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"COLLECTION_NAME": "your_collection_name",
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"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
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}
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}
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}
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```
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For local Qdrant mode:
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```json
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{
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"qdrant": {
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"command": "uvx",
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"args": ["mcp-server-qdrant"],
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"env": {
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"QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
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"COLLECTION_NAME": "your_collection_name",
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"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
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}
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}
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}
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```
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This MCP server will automatically create a collection with the specified name if it doesn't exist.
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By default, the server will use the `sentence-transformers/all-MiniLM-L6-v2` embedding model to encode memories.
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For the time being, only [FastEmbed](https://qdrant.github.io/fastembed/) models are supported.
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### Support for other tools
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This MCP server can be used with any MCP-compatible client. For example, you can use it with
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[Cursor](https://docs.cursor.com/context/model-context-protocol), which provides built-in support for the Model Context
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Protocol.
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## Environment Variables
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The configuration of the server is done using environment variables:
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- `QDRANT_URL`: URL of the Qdrant server, e.g. `http://localhost:6333`
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- `QDRANT_API_KEY`: API key for the Qdrant server (optional, depends on Qdrant server configuration)
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- `COLLECTION_NAME`: Name of the collection to use (required)
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- `EMBEDDING_MODEL`: Name of the embedding model to use (default: `sentence-transformers/all-MiniLM-L6-v2`)
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- `EMBEDDING_PROVIDER`: Embedding provider to use (currently only "fastembed" is supported)
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- `QDRANT_LOCAL_PATH`: Path to the local Qdrant database (alternative to `QDRANT_URL`)
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Note: You cannot provide both `QDRANT_URL` and `QDRANT_LOCAL_PATH` at the same time.
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> [!IMPORTANT]
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> Command-line arguments are not supported anymore! Please use environment variables for all configuration.
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## Contributing
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If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue!
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We'd love all and any contributions.
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### Testing `mcp-server-qdrant` locally
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The [MCP inspector](https://github.com/modelcontextprotocol/inspector) is a developer tool for testing and debugging MCP
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servers. It runs both a client UI (default port 5173) and an MCP proxy server (default port 3000). Open the client UI in
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your browser to use the inspector.
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```shell
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QDRANT_URL=":memory:" COLLECTION_NAME="test" \
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mcp dev src/mcp_server_qdrant/server.py
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```
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Once started, open your browser to http://localhost:5173 to access the inspector interface.
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## License
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This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the
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software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file
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in the project repository.
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