# Monte Carlo

> Use this tool when you need to monitor and troubleshoot production-grade AI agents, providing data and AI observability to identify and resolve issues. It solves problems related to agent performance, data quality, and context understanding, offering insights to improve overall system reliability. With inputs of agent data and outputs of actionable insights, use Monte Carlo in contexts where production-grade AI agent reliability and performance are critical.

Canonical page: https://skillsregistry.net/skills/io-github-monte-carlo-data-mcp  
JSON: https://api.skillsregistry.net/v1/skills/io-github-monte-carlo-data-mcp

## Description

Data + AI observability — monitor and troubleshoot production-grade agents and the context they use.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** unverified

## Facts

- **Version:** 1.1.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** monitoring
- **Updated:** 2026-04-19

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.monte-carlo-data%2Fmcp)

## Use it

MCP endpoint published by the skill: `https://mcp.getmontecarlo.com/mcp`

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": "io-github-monte-carlo-data-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-monte-carlo-data-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-monte-carlo-data-mcp/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