HumanSignal/label-studio-mcp-server

Overview

This MCP server provides a programmatic interface to create, manage, and automate Label Studio projects, tasks, and predictions. It uses the Model Context Protocol to integrate labeling workflows into applications and agents. The server is written in Python and communicates with the Label Studio API.

Best for

Best for
Developers building automated data labeling pipelines using Label Studio

Use cases

• Automate creation and assignment of labeling tasks from external data sources
• Integrate prediction model outputs directly into Label Studio projects
• Manage project configurations and labeling workflows programmatically

How to use

Tools exposed

• get_label_studio_projects_tool
• get_label_studio_project_details_tool
• get_label_studio_project_config_tool
• create_label_studio_project_tool
• update_label_studio_project_config_tool
• list_label_studio_project_tasks_tool
• get_label_studio_task_data_tool
• get_label_studio_task_annotations_tool
• import_label_studio_project_tasks_tool
• create_label_studio_prediction_tool

Tested with

Cursor, Claude Desktop

Example client config

{\n    "mcpServers": {\n        "label-studio": {\n            "command": "uvx",\n            "args": [\n                "--from",\n                "git+https://github.com/HumanSignal/label-studio-mcp-server",\n                "mcp-label-studio"\n            ],\n            "env": {\n                "LABEL_STUDIO_API_KEY": "your_actual_api_key_here",\n                "LABEL_STUDIO_URL": "http://localhost:8080"\n            }\n        }\n    }\n}

Notes

This MCP server provides a programmatic interface to create, manage, and automate Label Studio projects, tasks, and predictions. It uses the Model Context Protocol to integrate labeling workflows into applications and agents. The server is written in Python and communicates with the Label Studio API.

30 stars on GitHub. Last updated 2025-05-30. Licensed Apache-2.0.

Use cases

• Automate creation and assignment of labeling tasks from external data sources
• Integrate prediction model outputs directly into Label Studio projects
• Manage project configurations and labeling workflows programmatically

Pros

• Directly integrates with Label Studio’s API for full project lifecycle management
• Follows the Model Context Protocol, enabling extensibility and tool chaining
• Open source with a Python codebase, easy for developers to customize

Cons

• Small community (30 GitHub stars) limits available support and examples
• Requires a running Label Studio instance and Python environment
• Dependent on Label Studio API version compatibility

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.