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MCP server · AI

Trackio MCP

MCP (Model Context Protocol) server support for trackio experiment tracking

Unclaimed MIT last commit 9 months ago ai
51Fair

Scored 7 days ago · breakdown

About Trackio MCP

Trackio MCP is an MCP server published by fastmcp-me in the AI category: mCP (Model Context Protocol) server support for trackio experiment tracking.

Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.

Install

From source
git clone https://github.com/fastmcp-me/trackio-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • ✓Tests presentA test suite exists in the repository.
  • !Dependencies pinnedNo lockfile. Installs can pull different code over time.
  • ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

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trackio-mcp

PyPI version CI

MCP (Model Context Protocol) server support for trackio experiment tracking

This package enables AI agents to observe and interact with trackio experiments through the Model Context Protocol (MCP). Simply import trackio_mcp before trackio to automatically enable MCP server functionality.

Features

  • Zero-code integration: Just import trackio_mcp before trackio
  • Automatic MCP server: Enables MCP server on all trackio deployments (local & Spaces)
  • Rich tool set: Exposes trackio functionality as MCP tools for AI agents
  • Spaces compatible: Works seamlessly with Hugging Face Spaces deployments
  • Drop-in replacement: No changes needed to existing trackio code

Installation

pip install trackio-mcp

Or with development dependencies:

pip install trackio-mcp[dev]

Quick Start

Basic Usage

Simply import trackio_mcp before importing trackio:

import trackio_mcp  # This enables MCP server functionality
import trackio as wandb

# Your existing trackio code works unchanged
wandb.init(project="my-experiment")
wandb.log({"loss": 0.1, "accuracy": 0.95})
wandb.finish()

The MCP server will be automatically available at:

  • Local: http://localhost:7860/gradio_api/mcp/sse
  • Spaces: https://your-space.hf.space/gradio_api/mcp/sse

Deploy to Hugging Face Spaces with MCP

import trackio_mcp
import trackio as wandb

# Deploy to Spaces with MCP enabled automatically
wandb.init(
    project="my-experiment", 
    space_id="username/my-trackio-space"
)

wandb.log({"loss": 0.1})
wandb.finish()

Standalone MCP Server

Launch a dedicated MCP server for trackio tools:

from trackio_mcp.tools import launch_trackio_mcp_server

# Launch standalone MCP server on port 7861
launch_trackio_mcp_server(port=7861, share=False)

Available MCP Tools

Once connected, AI agents can use these trackio tools:

Core Tools (via Gradio API)

  • log: Log metrics to a trackio run
  • upload_db_to_space: Upload local database to a Space

Extended Tools (via trackio-mcp)

  • get_projects: List all trackio projects
  • get_runs: Get runs for a specific project
  • filter_runs: Filter runs by name pattern
  • get_run_metrics: Get metrics data for a specific run
  • get_available_metrics: Get all available metric names for a project
  • load_run_data: Load and process run data with optional smoothing
  • get_project_summary: Get comprehensive project statistics

Example Agent Interaction

Human: "Show me the latest results from my 'image-classification' project"

Agent: I'll check your trackio projects and get the latest results.

[Tool: get_projects] → finds "image-classification" project
[Tool: get_runs] → gets runs for "image-classification" 
[Tool: get_run_metrics] → gets metrics for latest run
[Tool: get_available_metrics] → gets metric names

Agent: Your latest image-classification run achieved 94.2% accuracy with a final loss of 0.18. The model trained for 50 epochs with best validation accuracy of 94.7% at epoch 45.

MCP Client Configuration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or equivalent:

Public Spaces:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-space.hf.space/gradio_api/mcp/sse"
    }
  }
}

Private Spaces/Datasets:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-private-space.hf.space/gradio_api/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_HF_TOKEN"
      }
    }
  }
}

Local Development:

{
  "mcpServers": {
    "trackio": {
      "url": "http://localhost:7860/gradio_api/mcp/sse"
    }
  }
}

See Claude Code MCP docs for more info.

Public Spaces:

claude mcp add --transport sse trackio https://your-space.hf.space/gradio_api/mcp/sse

Private Spaces/Datasets:

claude mcp add --transport sse --header "Authorization: Bearer YOUR_HF_TOKEN" trackio https://your-private-space.hf.space/gradio_api/mcp/sse

Local Development:

{
  "mcpServers": {
    "trackio": {
      "type": "sse",
      "url": "http://localhost:7860/gradio_api/mcp/sse"
    }
  }
}

Add to your Cursor ~/.cursor/mcp.json file or create .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.

Public Spaces:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-space.hf.space/gradio_api/mcp/sse"
    }
  }
}

Private Spaces/Datasets:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-private-space.hf.space/gradio_api/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_HF_TOKEN"
      }
    }
  }
}

Local Development:

{
  "mcpServers": {
    "trackio": {
      "url": "http://localhost:7860/gradio_api/mcp/sse"
    }
  }
}

Add to your Windsurf MCP config file. See Windsurf MCP docs for more info.

Public Spaces:

{
  "mcpServers": {
    "trackio": {
      "serverUrl": "https://your-space.hf.space/gradio_api/mcp/sse"
    }
  }
}

Private Spaces/Datasets:

{
  "mcpServers": {
    "trackio": {
      "serverUrl": "https://your-private-space.hf.space/gradio_api/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_HF_TOKEN"
      }
    }
  }
}

Local Development:

{
  "mcpServers": {
    "trackio": {
      "serverUrl": "http://localhost:7860/gradio_api/mcp/sse"
    }
  }
}

Add to .vscode/mcp.json. See VS Code MCP docs for more info.

Public Spaces:

{
  "mcp": {
    "servers": {
      "trackio": {
        "type": "http",
        "url": "https://your-space.hf.space/gradio_api/mcp/sse"
      }
    }
  }
}

Private Spaces/Datasets:

{
  "mcp": {
    "servers": {
      "trackio": {
        "type": "http", 
        "url": "https://your-private-space.hf.space/gradio_api/mcp/sse",
        "headers": {
          "Authorization": "Bearer YOUR_HF_TOKEN"
        }
      }
    }
  }
}

Local Development:

{
  "mcp": {
    "servers": {
      "trackio": {
        "type": "http",
        "url": "http://localhost:7860/gradio_api/mcp/sse"
      }
    }
  }
}

Add to mcp.json in your project directory. See Gemini CLI Configuration for details.

Public Spaces:

{
  "mcpServers": {
    "trackio": {
      "command": "npx",
      "args": ["mcp-remote", "https://your-space.hf.space/gradio_api/mcp/sse"]
    }
  }
}

Private Spaces/Datasets:

{
  "mcpServers": {
    "trackio": {
      "command": "npx", 
      "args": ["mcp-remote", "https://your-private-space.hf.space/gradio_api/mcp/sse"],
      "env": {
        "HF_TOKEN": "YOUR_HF_TOKEN"
      }
    }
  }
}

Local Development:

{
  "mcpServers": {
    "trackio": {
      "command": "npx",
      "args": ["mcp-remote", "http://localhost:7860/gradio_api/mcp/sse"]
    }
  }
}

Create .cursor/mcp.json (or equivalent for your IDE):

Public Spaces:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-space.hf.space/gradio_api/mcp/sse"
    }
  }
}

Private Spaces/Datasets:

{
  "mcpServers": {
    "trackio": {
      "url": "https://your-private-space.hf.space/gradio_api/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_HF_TOKEN"
      }
    }
  }
}

Local Development:

{
  "mcpServers": {
    "trackio": {
      "url": "http://localhost:7860/gradio_api/mcp/sse"
    }
  }
}

Configuration

Environment Variables

  • TRACKIO_DISABLE_MCP: Set to "true" to disable MCP functionality (default: MCP enabled)

Programmatic Control

import os
os.environ["TRACKIO_DISABLE_MCP"] = "true"  # Disable MCP
import trackio_mcp  # MCP won't be enabled
import trackio

How It Works

trackio-mcp uses monkey-patching to automatically:

  1. Enable MCP server: Sets mcp_server=True on all Gradio launches
  2. Enable API: Sets show_api=True to expose Gradio API endpoints
  3. Add tools: Registers additional trackio-specific MCP tools
  4. Preserve compatibility: No changes needed to existing trackio code

The package patches:

  • gradio.Blocks.launch() - Core Gradio launch method
  • trackio.ui.demo.launch() - Trackio dashboard launches
  • Adds new MCP endpoints at /gradio_api/mcp/sse

Deployment Examples

Local Development

import trackio_mcp
import trackio

# Start local tracking with MCP enabled
trackio.show()  # Dashboard + MCP server at http://localhost:7860

Public Spaces Deployment

import trackio_mcp
import trackio as wandb

# Deploy to public Spaces with MCP support
wandb.init(
    project="public-model",
    space_id="username/model-tracking"
)

wandb.log({"epoch": 1, "loss": 0.5})
wandb.finish()

Private Spaces/Datasets Deployment

import trackio_mcp
import trackio as wandb

# Deploy to private Spaces with private dataset
wandb.init(
    project="private-model",
    space_id="organization/private-model-tracking",  # Private space
    dataset_id="organization/private-model-metrics"  # Private dataset
)

wandb.log({"epoch": 1, "loss": 0.5})
wandb.finish()

CLI Interface

# Launch standalone MCP server
trackio-mcp server --port 7861

# Check status and configuration
trackio-mcp status

# Test MCP server functionality
trackio-mcp test --url http://localhost:7860

Security Considerations

  • Private Spaces: Use HF tokens for authentication with private spaces/datasets
  • Access Control: MCP server inherits trackio's access controls
  • Network Security: Consider firewall rules for production deployments
  • Token Management: Store HF tokens securely, use environment variables

Troubleshooting

MCP Server Not Available

import trackio_mcp
import trackio

# Check if MCP was disabled
import os
print("MCP Disabled:", os.getenv("TRACKIO_DISABLE_MCP"))

# Manual verification
trackio.show()  # Look for MCP server URL in output

Connection Issues

  1. Check URL: Ensure correct /gradio_api/mcp/sse endpoint
  2. Authentication: Add Bearer token for private Spaces/datasets
  3. Network: Verify firewall/proxy settings
  4. Dependencies: Ensure gradio[mcp] is installed

Tool Discovery Issues

# Test tools manually
from trackio_mcp.tools import register_trackio_tools

tools = register_trackio_tools()
tools.launch(mcp_server=True)  # Test tools interface

Contributing

  1. Fork the repository
  2. Install development dependencies: pip install -e .[dev]
  3. Make your changes
  4. Run tests: pytest
  5. Submit a pull request

License

MIT License - see LICENSE file.

Acknowledgments


Made with care for the AI research community

README fetched from the repository 2 hours ago. The original is authoritative.

Questions

About Trackio MCP

How do I install Trackio MCP?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Trackio MCP safe to use with an AI agent?

Its trust score is 51 out of 100 (fair). It passes 4 of the 6 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Trackio MCP still maintained?

The last commit was 9 months ago. That's long enough that you should check whether the maintainer is responding to issues before depending on it.