What is the Trackio MCP server?

Trackio is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.

How do MCP servers relate to agent skills?

Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.

How are reviews shown for Trackio?

This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

ai-mlanalytics-data

Trackio▌

by fcakyon

Trackio auto-exposes experiment tracking from Gradio apps—query ML projects, runs, metrics and stats without changing ex

Automatically monkey-patches Gradio applications to expose trackio experiment tracking data, enabling query access to machine learning projects, runs, metrics, and statistics without requiring code changes to existing workflows.

github stars

★ 3

GitHub npm

Zero code changes requiredWorks with Hugging Face SpacesAutomatic monkey-patching integration

best for

/ ML engineers tracking experiments in Gradio
/ Data scientists analyzing model performance
/ Teams monitoring ML workflows on Hugging Face Spaces

capabilities

/ Query experiment tracking data from Gradio apps
/ Access ML project runs and metrics
/ Retrieve experiment statistics and logs
/ Monitor trackio deployments automatically
/ Enable MCP server functionality on existing workflows

what it does

Automatically adds experiment tracking data access to Gradio applications without any code changes. Just import the package before trackio to enable querying ML experiments, runs, and metrics.

about

Trackio is a community-built MCP server published by fcakyon that provides AI assistants with tools and capabilities via the Model Context Protocol. Trackio auto-exposes experiment tracking from Gradio apps—query ML projects, runs, metrics and stats without changing ex It is categorized under ai ml, analytics data.

how to install

You can install Trackio in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Trackio is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

trackio-mcp

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

<details> <summary>Claude Desktop</summary>

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"
    }
  }
}

</details> <details> <summary>Claude Code</summary>

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"
    }
  }
}

</details> <details> <summary>Cursor</summary>

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"
    }
  }
}

</details> <details> <summary>Windsurf</summary>

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"
    }
  }
}

</details> <details> <summary>VS Code</summary>

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"
      }
    }
  }
}

</details> <details> <summary>Gemini CLI</summary>

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"]
    }
  }
}

</details> <details> <summary>Cline</summary>

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"
    }
  }
}

</details>

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:

Enable MCP server: Sets mcp_server=True on all Gradio launches
Enable API: Sets show_api=True to expose Gradio API endpoints
Add tools: Registers additional trackio-specific MCP tools
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": 

---

FAQ

What is the Trackio MCP server?: Trackio is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?: Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Trackio?: This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

MCP server reviews

Ratings

4.5★★★★★10 reviews

★★★★★Shikha Mishra· Oct 10, 2024
Trackio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
★★★★★Piyush G· Sep 9, 2024
We evaluated Trackio against two servers with overlapping tools; this profile had the clearer scope statement.
★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: Trackio is the kind of server we cite when onboarding engineers to host + tool permissions.
★★★★★Sakshi Patil· Jul 7, 2024
Trackio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
★★★★★Ganesh Mohane· Jun 6, 2024
I recommend Trackio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: Trackio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
★★★★★Dhruvi Jain· Apr 4, 2024
Trackio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
★★★★★Rahul Santra· Mar 3, 2024
According to our notes, Trackio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
★★★★★Pratham Ware· Feb 2, 2024
We wired Trackio into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
★★★★★Yash Thakker· Jan 1, 2024
Trackio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.