Label Studio MCP Server

# 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." <img src="./static/example.png" alt="Example usage of Label Studio MCP Server" width="600"> ## 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" } } } } ``` <!-- ## Installation Follow these instructions to install the server. ```bash git clone https://github.com/HumanSignal/label-studio-mcp-server.git cd label-studio-mcp-server # Install dependencies using uv uv venv source .venv/bin/activate uv sync ``` ```json { "mcpServers": { "label-studio": { "command": "uv", "args": [ "--directory", "/path/to/your/label-studio-mcp-server", // <-- Update this path "run", "label-studio-mcp.py" ], "env": { "LABEL_STUDIO_API_KEY": "your_actual_api_key_here", // <-- Your API key "LABEL_STUDIO_URL": "http://localhost:8080" } } } } ``` When configured this way, the `env` block injects the variables into the server process environment, and the script's `os.getenv()` calls will pick them up. --> ## 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).