https://github.com/HumanSignal/label-studio-mcp-server

https://github.com/HumanSignal/label-studio-mcp-server

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/HumanSignal/label-studio-mcp-server
• Owner: HumanSignal
• License: apache-2.0
• Created: 2025-04-25T15:19:31.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2025-05-30T10:03:53.000Z (5 months ago)
• Last Synced: 2025-06-16T07:11:14.772Z (5 months ago)
• Language: Python
• Size: 251 KB
• Stars: 8
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-mcp-servers - **Label Studio** - Open Source data labeling platform. `http` `git` `github` (📦 Other)
• awesome-mcp-servers - **HumanSignal/label-studio-mcp-server 🎖️ 🐍 ☁️ 🪟 🐧 🍎** - Create, manage, and automate Label Studio projects, tasks, and predictions for data labeling workflows. `http` `git` `github` (Other)

README

          
# Label Studio MCP Server

## Overview

This project provides a Model Context Protocol (MCP) server that allows interaction with a [Label Studio](https://labelstud.io/) instance using the `label-studio-sdk`. It enables programmatic management of labeling projects, tasks, and predictions via natural language or structured calls from MCP clients. Using this MCP Server, you can make requests like: 

* "Create a project in label studio with this data ..." 

* "How many tasks are labeled in my RAG review project?" 

* "Add predictions for my tasks." 

* "Update my labeling template to include a comment box." 



## Features

*   **Project Management**: Create, update, list, and view details/configurations of Label Studio projects.

*   **Task Management**: Import tasks from files, list tasks within projects, and retrieve task data/annotations.

*   **Prediction Integration**: Add model predictions to specific tasks.

*   **SDK Integration**: Leverages the official `label-studio-sdk` for communication.

## Prerequisites

1.  **Running Label Studio Instance:** You need a running instance of Label Studio accessible from where this MCP server will run.

2.  **API Key:** Obtain an API key from your user account settings in Label Studio.

## Configuration

The MCP server requires [the URL and API key for your Label Studio instance](https://labelstud.io/guide/access_tokens). If launching the server via an MCP client configuration file, you can specify the environment variables directly within the server definition. This is often preferred for client-managed servers.

Add the following JSON entry to your `claude_desktop_config.json` file or Cursor MCP settings:

```json

{

    "mcpServers": {

        "label-studio": {

            "command": "uvx",

            "args": [

                "--from",

                "git+https://github.com/HumanSignal/label-studio-mcp-server",

                "mcp-label-studio"

            ],

            "env": {

                "LABEL_STUDIO_API_KEY": "your_actual_api_key_here", // <-- Your API key

                "LABEL_STUDIO_URL": "http://localhost:8080"

            }

        }

    }

}

```

## Tools

The MCP server exposes the following tools:

### Project Management

*   **`get_label_studio_projects_tool()`**: Lists available projects (ID, title, task count).

*   **`get_label_studio_project_details_tool(project_id: int)`**: Retrieves detailed information for a specific project.

*   **`get_label_studio_project_config_tool(project_id: int)`**: Fetches the XML labeling configuration for a project.

*   **`create_label_studio_project_tool(title: str, label_config: str, ...)`**: Creates a new project with a title, XML config, and optional settings. Returns project details including a URL.

*   **`update_label_studio_project_config_tool(project_id: int, new_label_config: str)`**: Updates the XML labeling configuration for an existing project.

### Task Management

*   **`list_label_studio_project_tasks_tool(project_id: int)`**: Lists task IDs within a project (up to 100).

*   **`get_label_studio_task_data_tool(project_id: int, task_id: int)`**: Retrieves the data payload for a specific task.

*   **`get_label_studio_task_annotations_tool(project_id: int, task_id: int)`**: Fetches existing annotations for a specific task.

*   **`import_label_studio_project_tasks_tool(project_id: int, tasks_file_path: str)`**: Imports tasks from a JSON file (containing a list of task objects) into a project. Returns import summary and project URL.

### Predictions

*   **`create_label_studio_prediction_tool(task_id: int, result: List[Dict[str, Any]], ...)`**: Creates a prediction for a specific task. Requires the prediction result as a list of dictionaries matching the Label Studio format. Optional `model_version` and `score`.

## Example Use Case

1.  Create a new project using `create_label_studio_project_tool`.

2.  Prepare a JSON file (`tasks.json`) with task data.

3.  Import tasks using `import_label_studio_project_tasks_tool`, providing the project ID from step 1 and the path to `tasks.json`.

4.  List task IDs using `list_label_studio_project_tasks_tool`.

5.  Get data for a specific task using `get_label_studio_task_data_tool`.

6.  Generate a prediction result structure (list of dicts).

7.  Add the prediction using `create_label_studio_prediction_tool`.

## Contact

For questions or support, reach out via [GitHub Issues](https://github.com/HumanSignal/label-studio-mcp-server/issues).