Label Studio MCP Server

What is Label Studio MCP Server about?

Provides a Model Context Protocol (MCP) server that connects to a running Label Studio instance, allowing natural‑language or structured calls to manage projects, import tasks, retrieve annotations, and submit model predictions.

How to use Label Studio MCP Server?

1. Prepare a Label Studio instance and obtain an API key.
2. Configure the MCP server with the instance URL and API key (see serverConfig below).
3. Launch the server using the specified command; the server registers a set of tools.
4. Invoke tools from an MCP client (e.g., Claude Desktop, Cursor) to perform actions such as creating a project, importing tasks, or adding predictions.

Key Features of Label Studio MCP Server

• Project Management: list, create, view, and update project configurations.
• Task Management: import tasks from JSON files, list tasks, fetch task data and annotations.
• Prediction Integration: submit model predictions with optional versioning and scoring.
• SDK Leveraging: built on the official label-studio-sdk for reliable API communication.

Use Cases of Label Studio MCP Server

• Automated Dataset Preparation: script bulk import of labeling tasks from data pipelines.
• RAG Review Automation: query task counts and status via natural‑language prompts.
• Model Evaluation Loop: generate predictions programmatically and attach them to tasks for human review.
• Dynamic Project Configuration: update label schemas on‑the‑fly based on evolving annotation needs.

FAQ for Label Studio MCP Server

• Do I need to run Label Studio locally? No, any reachable instance (cloud‑hosted or on‑prem) works as long as you supply the URL and API key.
• Which programming language is required? The server is Python‑based; dependencies are managed with uv/uvx.
• How are environment variables passed? Include LABEL_STUDIO_API_KEY and LABEL_STUDIO_URL in the env block of the MCP server configuration.
• Can I limit the number of tasks returned? The list_label_studio_project_tasks_tool returns up to 100 task IDs per call.
• Where do I report issues? Open an issue on the GitHub repository: https://github.com/HumanSignal/label-studio-mcp-server/issues

Label Studio MCP Server

Overview

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."

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. 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"
            }
        }
    }
}

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.