mcp-label-studio: A Label Studio MCP server

Name: mcp-label-studio: A Label Studio MCP server
Author: lxkaka

@lxkaka

Repository

About mcp-label-studio: A Label Studio MCP server

mcp server for label studio

#mcp-label-studio #label-studio #data-labeling

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-label-studio": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

Tools

A list of all projects with their basic information

`project_id` (string): Label Studio project ID

Detailed project information including label configuration

`title` (string): Project title

New project information

`project_id` (string): Project ID

Updated project information

`project_id` (string): Project ID to delete

Deletion status

`project_id` (string): Project ID

Exported annotation data

`project_id` (string): Project ID

Import status

`project_id` (string): Project ID

List of supported export formats

Overview

What is mcp-label-studio?

mcp-label-studio is a Model Context Protocol (MCP) server that connects to Label Studio to manage and interact with annotation projects. It provides tools to create, update, delete projects, import tasks from files, and export annotations in multiple formats, enabling automated data labeling workflows for AI practitioners.

How to use mcp-label-studio?

Install via uv (recommended) by cloning the repository and running uv pip install -e . and uv run server.py, or use Docker with docker build and docker run setting the LABEL_STUDIO_API_KEY and LABEL_STUDIO_URL environment variables. Integrate with Claude Desktop by adding the server configuration (uv or docker variant) to claude_desktop_config.json.

Key features of mcp-label-studio

Create, update, delete annotation projects in Label Studio
List all projects and get detailed project information
Export project annotations (JSON, CSV, TSV, CONLL2003, COCO)
Import tasks from a file into a project
Retrieve supported export formats for a project

Use cases of mcp-label-studio

Automate the creation and maintenance of annotation projects for ML pipelines
Batch import labeling tasks from local files into Label Studio
Export completed annotations in various formats for downstream model training
Integrate labeling workflows into larger MCP-based AI applications

FAQ from mcp-label-studio

How do I install mcp-label-studio?

Use uv by cloning the repository, running uv pip install -e ., then uv run server.py. Alternatively, build a Docker image with docker build -t mcp-label-studio . and run it with the required environment variables.

What environment variables are required?

You must set LABEL_STUDIO_API_KEY (your Label Studio API key) and LABEL_STUDIO_URL (the URL of your Label Studio instance).

What tools does mcp-label-studio provide?

Eight tools: get_projects, get_project_detail, create_project, update_project, delete_project, export_project_annotations, import_tasks_from_file, and get_export_formats.

How can I debug the server?

Use the MCP inspector: LABEL_STUDIO_API_KEY=YOUR_API_KEY LABEL_STUDIO_URL=YOUR_URL npx @modelcontextprotocol/inspector uv run server.py from the project directory.

What license is mcp-label-studio under?

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

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