by fcakyon
Trackio auto-exposes experiment tracking from Gradio apps—query ML projects, runs, metrics and stats without changing ex
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.
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.
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.
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.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Trackio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired Trackio into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Trackio is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Trackio against two servers with overlapping tools; this profile had the clearer scope statement.
We wired Trackio into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Trackio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Trackio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: Trackio is the kind of server we cite when onboarding engineers to host + tool permissions.
Trackio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Trackio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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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.
trackio_mcp before trackiopip install trackio-mcp
Or with development dependencies:
pip install trackio-mcp[dev]
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:
http://localhost:7860/gradio_api/mcp/ssehttps://your-space.hf.space/gradio_api/mcp/sseimport 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()
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)
Once connected, AI agents can use these trackio tools:
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.
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><b>Claude Code</b></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><b>Cursor</b></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><b>Windsurf</b></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><b>VS Code</b></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><b>Gemini CLI</b></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><b>Cline</b></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>
TRACKIO_DISABLE_MCP: Set to "true" to disable MCP functionality (default: MCP enabled)import os
os.environ["TRACKIO_DISABLE_MCP"] = "true" # Disable MCP
import trackio_mcp # MCP won't be enabled
import trackio
trackio-mcp uses monkey-patching to automatically:
mcp_server=True on all Gradio launchesshow_api=True to expose Gradio API endpointsThe package patches:
gradio.Blocks.launch() - Core Gradio launch methodtrackio.ui.demo.launch() - Trackio dashboard launches/gradio_api/mcp/sseimport trackio_mcp
import trackio
# Start local tracking with MCP enabled
trackio.show() # Dashboard + MCP server at http://localhost:7860
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":
---
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.