# MCP ML Monitor

> Use this tool when you need to monitor and maintain the performance of machine learning models in production, solving problems of data drift and performance degradation. It takes in model performance data and outputs automated alerts and retraining recommendations. Ideal for use in production environments where ML models are deployed, to ensure continuous reliability and accuracy.

Canonical page: https://skillsregistry.net/skills/rishi625-mcp-ml-monitor  
JSON: https://api.skillsregistry.net/v1/skills/rishi625-mcp-ml-monitor

## Description

Monitors ML models in production for data drift and performance degradation, providing automated alerts and retraining recommendations.

## Trust

- **Trust score (0–1):** 0.66
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** monitoring
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/d6rgzon28r)
- **Repository:** <https://github.com/Rishi625/mcp-ml-monitor>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "rishi625-mcp-ml-monitor"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rishi625-mcp-ml-monitor` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rishi625-mcp-ml-monitor/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt