Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/HumanSignal/label-studio-mcp-server below:

GitHub - HumanSignal/label-studio-mcp-server

This project provides a Model Context Protocol (MCP) server that allows interaction with a Label Studio 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."

• 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.

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.

The MCP server requires the URL and API key for your Label Studio instance. 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:

{
    "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"
            }
        }
    }
}

The MCP server exposes the following tools:

• 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.

• 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.

• 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.

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.

For questions or support, reach out via GitHub Issues.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4