> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trainy.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# MCP Integration
> Connect Pluto to AI coding assistants like Claude Code via the Model Context Protocol.
**New & Experimental** - MCP support is a new feature. Please report any issues to [support@trainy.ai](mailto:support@trainy.ai) or on [Discord](https://discord.com/invite/HQUBJSVgAP).
# 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
## What is MCP?
The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 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.
## Setup with Claude Code
### Prerequisites
* A Pluto account with an API key ([get one here](https://pluto.trainy.ai))
* Claude Code CLI installed
### Step 1: Get Your API Key
1. Go to [pluto.trainy.ai](https://pluto.trainy.ai) and sign in
2. Navigate to **Settings > Developers > API Keys**
3. Create a new API key and copy it
### Step 2: Configure Claude Code
Run the following command to add the Pluto MCP server:
```bash theme={null}
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`:
```json theme={null}
{
"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 .`
### Step 3: Restart Claude Code
After saving the configuration, restart Claude Code for the changes to take effect:
```bash theme={null}
claude
```
You should see the Pluto tools become available. You can verify by asking Claude: "What Pluto projects do I have?"
## Available Tools
Once connected, the following tools become available to your AI assistant:
| Tool | Description |
| ---------------- | ----------------------------------------------------------- |
| `list_projects` | List all projects in your organization |
| `list_runs` | Search and filter experiment runs by project, name, or tags |
| `get_run` | Get detailed information about a specific run |
| `query_logs` | Query console logs (stdout/stderr) from a run |
| `query_metrics` | Query time-series metrics (loss, accuracy, etc.) |
| `get_files` | Get files and artifacts from a run |
| `get_statistics` | Get statistics and anomaly detection for metrics |
| `compare_runs` | Compare a metric across multiple runs |
## 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?"
## Claude Code Web
To make the pluto MCP available to your Claude Code web UI and cloud agents, visit:
[https://claude.ai/settings/connectors?modal=add-custom-connector](https://claude.ai/settings/connectors?modal=add-custom-connector)
From here you can enter the following information for the Pluto MCP server:
* Name: Pluto
* Remote MCP Server URL: [https://pluto-mcp.trainy.ai/mcp](https://pluto-mcp.trainy.ai/mcp)
Afterwards, click on **Connect** and you will be redirected to an OAuth flow where you can enter the Pluto API key you want to use for the MCP connection.
## Feedback
MCP integration is experimental. We'd love to hear your feedback:
* **Discord**: [Join our community](https://discord.com/invite/HQUBJSVgAP)
* **Email**: [support@trainy.ai](mailto:support@trainy.ai)