mcp-label-studio: A Label Studio MCP server

mcp-label-studio: A Label Studio MCP server

By lxkaka GitHub

mcp server for label studio

mcp-label-studio label-studio
Overview

What is mcp-label-studio?

mcp-label-studio is a Model Context Protocol (MCP) server designed for managing and interacting with Label Studio projects, facilitating the data labeling workflow.

How to use mcp-label-studio?

To use mcp-label-studio, you can either run it using uv or Docker. Clone the repository, install the necessary dependencies, and run the server to start managing your annotation projects.

Key features of mcp-label-studio?

  • Create and manage annotation projects
  • Import and export data in various formats
  • Tools for project management including getting project details, updating, and deleting projects
  • Support for multiple export formats like JSON, CSV, and COCO

Use cases of mcp-label-studio?

  1. Managing large-scale data labeling projects
  2. Collaborating with teams on annotation tasks
  3. Exporting labeled data for machine learning model training

FAQ from mcp-label-studio?

  • What is the purpose of mcp-label-studio?

It is designed to streamline the data labeling process for projects using Label Studio.

  • Can I run mcp-label-studio using Docker?

Yes! You can run it using Docker by building the image and running the container with the necessary environment variables.

  • Is there a license for mcp-label-studio?

Yes, it is licensed under the MIT License, allowing free use, modification, and distribution.

Content

mcp-label-studio: A Label Studio MCP server

Overview

A Model Context Protocol server for managing and interacting with Label Studio projects. This server provides tools to create and manage annotation projects, import and export data, and facilitate the data labeling workflow.

Tools

  1. get_projects

    • Get all projects information
    • Returns: A list of all projects with their basic information
  2. get_project_detail

    • Get detailed information about a specific project
    • Input:
      • project_id (string): Label Studio project ID
    • Returns: Detailed project information including label configuration
  3. create_project

    • Create a new annotation project
    • Input:
      • title (string): Project title
      • description (string, optional): Project description
      • label_config (string, optional): XML label configuration
    • Returns: New project information
  4. update_project

    • Update an existing project
    • Input:
      • project_id (string): Project ID
      • title (string, optional): New project title
      • description (string, optional): New project description
      • label_config (string, optional): New XML label configuration
    • Returns: Updated project information
  5. delete_project

    • Delete a project
    • Input:
      • project_id (string): Project ID to delete
    • Returns: Deletion status
  6. export_project_annotations

    • Export project annotation data
    • Input:
      • project_id (string): Project ID
      • export_format (string, optional): Format to export ("JSON", "CSV", "TSV", "CONLL2003", "COCO")
      • output_path (string, optional): Path to save the export file
    • Returns: Exported annotation data
  7. import_tasks_from_file

    • Import tasks from a file
    • Input:
      • project_id (string): Project ID
      • file_path (string): Path to the import file
    • Returns: Import status
  8. get_export_formats

    • Get supported export formats for a project
    • Input:
      • project_id (string): Project ID
    • Returns: List of supported export formats

Installation

When using uv, you can run the server directly:

git clone https://github.com/yourusername/mcp-label-studio.git
cd mcp-label-studio
uv pip install -e .
uv run server.py

Using Docker

You can also use Docker to run the server:

docker build -t mcp-label-studio .
docker run -e LABEL_STUDIO_API_KEY=your-api-key -e LABEL_STUDIO_URL=http://your-label-studio-instance mcp-label-studio

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Using uv
"mcpServers": {
  "label-studio": {
    "command": "uv",
    "args": ["run", "/path/to/mcp-label-studio/server.py"],
    "env": {
      "LABEL_STUDIO_API_KEY": "YOUR_LABEL_STUDIO_API_KEY",
      "LABEL_STUDIO_URL": "YOUR_LABEL_STUDIO_URL"
    }
  }
}
Using docker
"mcpServers": {
  "label-studio": {
    "command": "docker",
    "args": ["run", "-i", "--rm", "-e", "LABEL_STUDIO_API_KEY=YOUR_API_KEY", "-e", "LABEL_STUDIO_URL=YOUR_URL", "mcp-label-studio"]
  }
}

Debugging

You can use the MCP inspector to debug the server:

cd /path/to/mcp-label-studio
LABEL_STUDIO_API_KEY=YOUR_API_KEY LABEL_STUDIO_URL=YOUR_URL npx @modelcontextprotocol/inspector uv run server.py

License

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

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