# Perfetto

> Use this tool when you need to analyze performance traces in Perfetto's .pftrace format, solving problems such as optimizing system performance and troubleshooting issues. It takes .pftrace files and PerfettoSQL queries as inputs and outputs analyzed process and thread information, allowing for efficient inspection of system performance. Use Perfetto in contexts where multi-turn agentic workflows are required, such as with Claude Code and other MCP clients.

Canonical page: https://skillsregistry.net/skills/0xzone-perfetto  
JSON: https://api.skillsregistry.net/v1/skills/0xzone-perfetto

## Description

Perfetto MCP is a Rust-based server that enables AI assistants to analyze performance traces in Perfetto's .pftrace format using PerfettoSQL queries. It wraps Google's trace_processor_shell backend and exposes tools for loading trace files, listing available tables and views, inspecting schemas, executing SQL queries, and analyzing process and thread information. Designed for multi-turn agentic workflows with Claude Code and other MCP clients.

## Trust

- **Trust score (0–1):** 0.31
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/0xzone-perfetto)
- **Repository:** <https://github.com/tooluse-labs/perfetto-mcp-rs>

## 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": "0xzone-perfetto"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/0xzone-perfetto` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/0xzone-perfetto/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