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New & Experimental - MCP support is a new feature. Please report any issues to [email protected] or on Discord.

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Model Context Protocol (MCP) Integration

The Pluto MCP server allows AI coding assistants to directly query your ML experiment data. This enables powerful workflows like:
  • Asking your AI assistant to analyze training runs and identify issues
  • Comparing metrics across experiments in natural language
  • Debugging failed runs by querying logs and metrics
  • Getting insights about your ML experiments without leaving your editor

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What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely access external data sources. With Pluto’s MCP server, tools like Claude Code can directly query your experiment data, metrics, and logs.

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Setup with Claude Code

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Prerequisites

  • A Pluto account with an API key (get one here)
  • Claude Code CLI installed

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Step 1: Get Your API Key

  1. Go to pluto.trainy.ai and sign in
  2. Navigate to Settings > Developers > API Keys
  3. Create a new API key and copy it

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Step 2: Configure Claude Code

Run the following command to add the Pluto MCP server: 
claude mcp add pluto --transport http --header "Authorization: Bearer mlpi_xxxxxxxxxx" -- https://pluto-mcp.trainy.ai/mcp/
Replace mlpi_xxxxxxxxxx with the API key you copied in Step 1.
You can also manually edit the MCP settings file at ~/.claude/mcp_settings.json:
{
  "mcpServers": {
    "pluto": {
      "type": "http",
      "url": "https://pluto-mcp.trainy.ai/mcp/",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}
If the file doesn’t exist, create it. Make sure the JSON is valid - you can verify with cat ~/.claude/mcp_settings.json | jq .

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Step 3: Restart Claude Code

After saving the configuration, restart Claude Code for the changes to take effect: 
claude
You should see the Pluto tools become available. You can verify by asking Claude: “What Pluto projects do I have?”

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Available Tools

Once connected, the following tools become available to your AI assistant:
ToolDescription
list_projectsList all projects in your organization
list_runsSearch and filter experiment runs by project, name, or tags
get_runGet detailed information about a specific run
query_logsQuery console logs (stdout/stderr) from a run
query_metricsQuery time-series metrics (loss, accuracy, etc.)
get_filesGet files and artifacts from a run
get_statisticsGet statistics and anomaly detection for metrics
compare_runsCompare a metric across multiple runs

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Example Prompts

Once configured, you can interact with your Pluto data naturally through your AI assistant: List your projects:
“What ML projects do I have in Pluto?”
Find recent runs:
“Show me the last 5 training runs in the gpt-finetuning project”
Analyze a run:
“What was the final loss for run 1234? Did it converge?”
Compare experiments:
“Compare the train/loss between runs 100, 101, and 102 - which performed best?”
Debug failures:
“Show me the error logs from run 456 - why did it fail?”
Get insights:
“Are there any anomalies in the metrics for my latest run?”

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Feedback

MCP integration is experimental. We’d love to hear your feedback: