mcp-server-qdrant-pro 0.1.4

MCP server for Qdrant vector database - supports personal memory management and enterprise GitHub codebase search. Extended from original mcp-server-qdrant package

mcp-server-qdrant: A Qdrant MCP server

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.

Overview

A Model Context Protocol server for advanced GitHub codebase search using Qdrant vector search engine. It provides semantic code search capabilities across vectorized GitHub repositories.

Features

• Repository-scoped search: Always filtered by repository for focused results
• Semantic code search: Find functionality patterns across codebases
• Code pattern analysis: Understand repository structure and common patterns
• Implementation discovery: Find examples of specific functionality
• Rich metadata filtering: Filter by programming language, themes, complexity, file types, and more
• Hierarchical filtering: Repository → themes → refinement filters for optimal search experience

Components

Tools

1. search-repository

   • Search for code patterns and implementations within a specific GitHub repository
   • Input:
     • repository_id (string, required): Repository identifier in format 'owner/repo' (e.g., 'taika-st/dtna-chat')
     • query (string): Semantic search query for finding code patterns, functionality, or implementations
     • themes (string, optional): JSON array of code themes/patterns to match (e.g., '["authentication", "database"]')
     • programming_language (string, optional): Filter by programming language
     • complexity_score (integer, optional): Minimum complexity score
     • Additional filterable fields: file_type, directory, has_code_patterns, etc.
   • Returns: Formatted code snippets with rich metadata

2. analyze-repository-patterns

   • Analyze code patterns, themes, and architecture within a repository
   • Input:
     • repository_id (string, required): Repository identifier
     • themes (string, optional): JSON array of specific themes to analyze
     • programming_language (string, optional): Focus on specific language
     • directory (string, optional): Analyze specific directory
   • Returns: Repository analysis with statistics and insights

3. find-repository-implementations

   • Find implementations of specific patterns or functionality within a repository
   • Input:
     • repository_id (string, required): Repository identifier
     • pattern_query (string): Description of pattern to find (e.g., 'user authentication', 'database connection')
     • themes (string, optional): JSON array of expected themes for filtering
     • programming_language (string, optional): Expected programming language
     • min_complexity (integer, optional): Minimum complexity threshold
   • Returns: Implementations ranked by semantic similarity

Environment Variables

The configuration of the server is done using environment variables:

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 collection containing vectorized GitHub repositories	None
QDRANT_LOCAL_PATH	Path to the local Qdrant database (alternative to QDRANT_URL)	None
QDRANT_SEARCH_LIMIT	Maximum results per search operation	10
QDRANT_ALLOW_ARBITRARY_FILTER	Allow arbitrary filter conditions in queries	false
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_SEARCH_REPOSITORY_DESCRIPTION	Custom description for the search-repository tool	See default in settings.py
TOOL_ANALYZE_PATTERNS_DESCRIPTION	Custom description for the analyze-repository-patterns tool	See default in settings.py
TOOL_FIND_IMPLEMENTATIONS_DESCRIPTION	Custom description for the find-repository-implementations tool	See default in settings.py

GitHub Codebase Search Configuration

The server is designed for searching vectorized GitHub repositories. Recommended configuration:

Setting	Recommended Value	Purpose
COLLECTION_NAME	github-codebases or similar descriptive name	Collection with vectorized repositories
QDRANT_SEARCH_LIMIT	10-50 depending on use case	Balance between relevance and performance
QDRANT_ALLOW_ARBITRARY_FILTER	false (recommended for security)	Restrict to predefined filter fields

Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

[!IMPORTANT] Command-line arguments are not supported anymore! Please use environment variables for all configuration.

FastMCP Environment Variables

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_DEBUG	Enable debug mode	false
FASTMCP_LOG_LEVEL	Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)	INFO
FASTMCP_HOST	Host address to bind the server to	127.0.0.1
FASTMCP_PORT	Port to run the server on	8000
FASTMCP_WARN_ON_DUPLICATE_RESOURCES	Show warnings for duplicate resources	true
FASTMCP_WARN_ON_DUPLICATE_TOOLS	Show warnings for duplicate tools	true
FASTMCP_WARN_ON_DUPLICATE_PROMPTS	Show warnings for duplicate prompts	true
FASTMCP_DEPENDENCIES	List of dependencies to install in the server environment	[]

Installation

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant

Transport Protocols

The server supports different transport protocols that can be specified using the --transport flag:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse

Supported transport protocols:

• stdio (default): Standard input/output transport, might only be used by local MCP clients
• sse: Server-Sent Events transport, perfect for remote clients
• streamable-http: Streamable HTTP transport, perfect for remote clients, more recent than SSE

The default transport is stdio if not specified.

When SSE transport is used, the server will listen on the specified port and wait for incoming connections. The default port is 8000, however it can be changed using the FASTMCP_PORT environment variable.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
FASTMCP_PORT=1234 \
uvx mcp-server-qdrant --transport sse

Using Docker

A Dockerfile is available for building and running the MCP server:

# Build the container
docker build -t mcp-server-qdrant .

# Run the container
docker run -p 8000:8000 \
  -e FASTMCP_HOST="0.0.0.0" \
  -e QDRANT_URL="http://your-qdrant-server:6333" \
  -e QDRANT_API_KEY="your-api-key" \
  -e COLLECTION_NAME="your-collection" \
  mcp-server-qdrant

[!TIP] Please note that we set FASTMCP_HOST="0.0.0.0" to make the server listen on all network interfaces. This is necessary when running the server in a Docker container.

Installing via Smithery

To install Qdrant MCP Server for Claude Desktop automatically via Smithery:

npx @smithery/cli install mcp-server-qdrant --client claude

Manual configuration of Claude Desktop

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.

Support for other tools

This MCP server can be used with any MCP-compatible client. For example, you can use it with Cursor and VS Code, which provide built-in support for the Model Context Protocol.

Enterprise Mode Examples

Personal Memory Mode (Default)

{
  "mcpServers": {
    "qdrant": {
      "command": "uvx",
      "args": ["mcp-server-qdrant"],
      "env": {
        "QDRANT_URL": "https://your-qdrant-cluster.com",
        "QDRANT_API_KEY": "your-api-key",
        "COLLECTION_NAME": "personal-memories"
      }
    }
  }
}

Enterprise GitHub Codebase Search Mode

{
  "mcpServers": {
    "qdrant-enterprise": {
      "command": "uvx",
      "args": ["mcp-server-qdrant"],
      "env": {
        "ENTERPRISE_MODE": "true",
        "QDRANT_URL": "https://your-qdrant-cluster.com",
        "QDRANT_API_KEY": "your-api-key",
        "COLLECTION_NAME": "github-codebases",
        "QDRANT_READ_ONLY": "true",
        "QDRANT_SEARCH_LIMIT": "10"
      }
    }
  }
}

Using with Cursor/Windsurf

You can configure this MCP server to work as a code search tool for Cursor or Windsurf by customizing the tool descriptions:

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="code-snippets" \
TOOL_STORE_DESCRIPTION="Store reusable code snippets for later retrieval. \
The 'information' parameter should contain a natural language description of what the code does, \
while the actual code should be included in the 'metadata' parameter as a 'code' property. \
The value of 'metadata' is a Python dictionary with strings as keys. \
Use this whenever you generate some code snippet." \
TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions. \
The 'query' parameter should describe what you're looking for, \
and the tool will return the most relevant code snippets. \
Use this when you need to find existing code snippets for reuse or reference." \
uvx mcp-server-qdrant --transport sse # Enable SSE transport

In Cursor/Windsurf, you can then configure the MCP server in your settings by pointing to this running server using SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the Cursor documentation. If you are running Cursor/Windsurf locally, you can use the following URL:

http://localhost:8000/sse

[!TIP] We suggest SSE transport as a preferred way to connect Cursor/Windsurf to the MCP server, as it can support remote connections. That makes it easy to share the server with your team or use it in a cloud environment.

This configuration transforms the Qdrant MCP server into a specialized code search tool that can:

1. Store code snippets, documentation, and implementation details
2. Retrieve relevant code examples based on semantic search
3. Help developers find specific implementations or usage patterns

You can populate the database by storing natural language descriptions of code snippets (in the information parameter) along with the actual code (in the metadata.code property), and then search for them using natural language queries that describe what you're looking for.

[!NOTE] The tool descriptions provided above are examples and may need to be customized for your specific use case. Consider adjusting the descriptions to better match your team's workflow and the specific types of code snippets you want to store and retrieve.

If you have successfully installed the mcp-server-qdrant, but still can't get it to work with Cursor, please consider creating the Cursor rules so the MCP tools are always used when the agent produces a new code snippet. You can restrict the rules to only work for certain file types, to avoid using the MCP server for the documentation or other types of content.

Using with Claude Code

You can enhance Claude Code's capabilities by connecting it to this MCP server, enabling semantic search over your existing codebase.

Setting up mcp-server-qdrant

1. Add the MCP server to Claude Code:

   # Add mcp-server-qdrant configured for code search
   claude mcp add code-search \
   -e QDRANT_URL="http://localhost:6333" \
   -e COLLECTION_NAME="code-repository" \
   -e EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
   -e TOOL_STORE_DESCRIPTION="Store code snippets with descriptions. The 'information' parameter should contain a natural language description of what the code does, while the actual code should be included in the 'metadata' parameter as a 'code' property." \
   -e TOOL_FIND_DESCRIPTION="Search for relevant code snippets using natural language. The 'query' parameter should describe the functionality you're looking for." \
   -- uvx mcp-server-qdrant
   

2. Verify the server was added:

   claude mcp list
   

Using Semantic Code Search in Claude Code

Tool descriptions, specified in TOOL_STORE_DESCRIPTION and TOOL_FIND_DESCRIPTION, guide Claude Code on how to use the MCP server. The ones provided above are examples and may need to be customized for your specific use case. However, Claude Code should be already able to:

1. Use the qdrant-store tool to store code snippets with descriptions.
2. Use the qdrant-find tool to search for relevant code snippets using natural language.

Run MCP server in Development Mode

The MCP server can be run in development mode using the mcp dev command. This will start the server and open the MCP inspector in your browser.

COLLECTION_NAME=mcp-dev fastmcp dev src/mcp_server_qdrant/server.py

Using with VS Code

For one-click installation, click one of the install buttons below:

Manual Installation

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}"
      }
    }
  }
}

Contributing

If you have suggestions for how mcp-server-qdrant could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

Development Setup

For rapid iteration during development:

# Install the project in editable mode
uv pip install -e .

# Test with MCP Inspector
npx @modelcontextprotocol/inspector uv run mcp-server-qdrant

Publishing to PyPI

When extending or forking this project, ensure you have a unique package name:

1. Update pyproject.toml with a unique name (e.g., mcp-server-qdrant-pro):

   [project]
   name = "mcp-server-qdrant-pro"
   
   [project.scripts]
   mcp-server-qdrant-pro = "mcp_server_qdrant.main:main"
   
   [tool.hatch.build.targets.wheel]
   packages = ["src/mcp_server_qdrant"]
   

2. Build the package (requires PyPI account and API token):

   # Build without including source files
   uv build --no-sources
   

3. Publish to PyPI:

   # Note: API token must be scoped to "all projects" for first-time publishers
   uv publish --token pypi-yourtoken
   

Testing mcp-server-qdrant-pro locally

The MCP inspector is the recommended tool for testing:

# Using development mode
npx @modelcontextprotocol/inspector uv run mcp-server-qdrant-pro

# For enterprise mode testing
ENTERPRISE_MODE=true COLLECTION_NAME="test" QDRANT_LOCAL_PATH="/tmp/test-storage" \
npx @modelcontextprotocol/inspector uv run mcp-server-qdrant-pro

License

This MCP server is licensed under the Apache License 2.0. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the Apache License 2.0. For more details, please see the LICENSE file in the project repository.