# perfetto-mcp

> Use this tool when you need to manage and analyze performance data from various sources, solving problems related to system optimization and bottleneck identification. It takes in performance tracing data as input and outputs actionable insights, allowing for informed decision-making. Ideal for use in development and testing contexts where performance monitoring is crucial.

Canonical page: https://skillsregistry.net/skills/antarikshc-perfetto-mcp  
JSON: https://api.skillsregistry.net/v1/skills/antarikshc-perfetto-mcp

## Trust

- **Trust score (0–1):** 0.88
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/antarikshc/perfetto-mcp)
- **Repository:** <https://github.com/antarikshc/perfetto-mcp>

## 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": "antarikshc-perfetto-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/antarikshc-perfetto-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/antarikshc-perfetto-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