Label Studio MCP Server

What is Label Studio MCP Server about?

Enables natural‑language or structured calls to a running Label Studio instance, allowing creation, updating, and querying of projects, tasks, and model predictions without manual UI interaction.

How to use Label Studio MCP Server?

1. Prerequisites: Have a Label Studio instance reachable from the server host and obtain an API key from the user settings.
2. Configuration: Define the server in an MCP client configuration (e.g., claude_desktop_config.json or Cursor MCP settings) and supply LABEL_STUDIO_URL and LABEL_STUDIO_API_KEY as environment variables.
3. Start the server: Launch via the specified command (e.g., uvx ... mcp-label-studio).
4. Call tools: Use the exposed tools such as create_label_studio_project_tool, import_label_studio_project_tasks_tool, and create_label_studio_prediction_tool through the MCP client to interact with Label Studio.

Key features of Label Studio MCP Server

• Project Management: List, create, retrieve details, and update XML labeling configurations.
• Task Management: Import tasks from JSON files, list task IDs, fetch task data and annotations.
• Prediction Integration: Attach model predictions to specific tasks with optional version and confidence score.
• SDK Integration: Built on the official label-studio-sdk for reliable API communication.
• Environment‑driven configuration: Simple env block injection for API key and server URL.

Use cases of Label Studio MCP Server

• Automating bulk project setup for new labeling campaigns.
• Programmatically importing large task datasets generated by upstream pipelines.
• Adding AI model predictions to tasks for human‑in‑the‑loop review.
• Querying annotation statistics (e.g., number of labeled tasks) from conversational agents.
• Dynamically updating labeling schemas without manual UI edits.

FAQ from the Label Studio MCP Server

Q: Do I need to run Label Studio locally? A: No, any reachable Label Studio instance works; just provide its URL.

Q: Where do I obtain the API key? A: Generate it in Label Studio under User Settings → Access Tokens.

Q: Which environment variables are required? A: LABEL_STUDIO_URL (e.g., http://localhost:8080) and LABEL_STUDIO_API_KEY.

Q: How are tasks imported? A: Provide a path to a JSON file containing a list of task objects to import_label_studio_project_tasks_tool.

Q: Can I update the labeling configuration after project creation? A: Yes, use update_label_studio_project_config_tool with the new XML config.

Q: What format should predictions follow? A: A list of dictionaries matching the Label Studio result schema; optional model_version and score can be supplied.

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.