Perfetto

Turn natural language into powerful Perfetto trace analysis. Quickly explain jank, diagnose ANRs, spot CPU hot threads, uncover lock contention, and find memory leaks.

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Perfetto MCP

Turn natural language into powerful Perfetto trace analysis

A Model Context Protocol (MCP) server that transforms natural-language prompts into focused Perfetto analyses. Quickly explain jank, diagnose ANRs, spot CPU hot threads, uncover lock contention, and find memory leaks – all without writing SQL.

✨ Features

  • Natural Language → SQL: Ask questions in plain English, get precise Perfetto queries
  • ANR Detection: Automatically identify and analyze Application Not Responding events
  • Performance Analysis: CPU profiling, frame jank detection, memory leak detection
  • Thread Contention: Find synchronization bottlenecks and lock contention
  • Binder Profiling: Analyze IPC performance and slow system interactions

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📋 Prerequisites

  • Python 3.13+ (macOS/Homebrew): 
    brew install [email protected]
  • uv (recommended): 
    brew install uv

🚀 Getting Started

Cursor

Install MCP Server

Or add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project):

{
  "mcpServers": {
    "perfetto-mcp": {
      "command": "uvx",
      "args": ["perfetto-mcp"]
    }
  }
}
Claude Code

Run this command. See Claude Code MCP docs for more info.

# Add to user scope
claude mcp add perfetto-mcp --scope user -- uvx perfetto-mcp

Or edit ~/claude.json (macOS) or %APPDATA%\Claude\claude.json (Windows):

{
  "mcpServers": {
    "perfetto-mcp": {
      "command": "uvx",
      "args": ["perfetto-mcp"]
    }
  }
}
VS Code

Install in VS Code

or add to .vscode/mcp.json (project) or run "MCP: Add Server" command:

{
  "mcpServers": {
    "perfetto-mcp": {
      "command": "uvx",
      "args": ["perfetto-mcp"]
    }
  }
}

Enable in GitHub Copilot Chat's Agent mode.

Codex

Edit ~/.codex/config.toml:

[mcp_servers.perfetto-mcp]
command = "uvx"
args = ["perfetto-mcp"]

Local Install (development server)

cd perfetto-mcp-server
uv sync
uv run mcp dev src/perfetto_mcp/dev.py
Local MCP 
{
  "mcpServers": {
    "perfetto-mcp-local": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/git/repo/perfetto-mcp",
        "run",
        "-m",
        "perfetto_mcp"
      ],
      "env": { "PYTHONPATH": "src" }
    }
  }
}
Using pip 
pip3 install perfetto-mcp
python3 -m perfetto_mcp

📖 How to Use

Example starting prompt:

In the perfetto trace, I see that the FragmentManager is taking 438ms to execute. Can you figure out why it's taking so long?

Required Parameters

Every tool needs these two inputs:

ParameterDescriptionExample
trace_pathAbsolute path to your Perfetto trace/path/to/trace.perfetto-trace
process_nameTarget process/app namecom.example.app

In Your Prompts

Be explicit about the trace and process, prefix your prompt with:

"Use perfetto trace /absolute/path/to/trace.perfetto-trace for process com.example.app"

Optional Filters

Many tools support additional filtering (but let your LLM handle that):

  • time_range: {start_ms: 10000, end_ms: 25000}
  • Tool-specific thresholds: min_block_ms, jank_threshold_ms, limit

🛠️ Available Tools

🔎 Exploration & Discovery

ToolPurposeExample Prompt
find_slicesSurvey slice names and locate hot paths"Find slice names containing 'Choreographer' and show top examples"
execute_sql_queryRun custom PerfettoSQL for advanced analysis"Run custom SQL to correlate threads and frames in the first 30s"

🚨 ANR Analysis

Note: Helpful if the recorded trace contains ANR

ToolPurposeExample Prompt
detect_anrsFind ANR events with severity classification"Detect ANRs in the first 10s and summarize severity"
anr_root_cause_analyzerDeep-dive ANR causes with ranked likelihood"Analyze ANR root cause around 20,000 ms and rank likely causes"

🎯 Performance Profiling

ToolPurposeExample Prompt
cpu_utilization_profilerThread-level CPU usage and scheduling"Profile CPU usage by thread and flag the hottest threads"
main_thread_hotspot_slicesFind longest-running main thread operations"List main-thread hotspots >50 ms during 10s–25s"

📱 UI Performance

ToolPurposeExample Prompt
detect_jank_framesIdentify frames missing deadlines"Find janky frames above 16.67 ms and list the worst 20"
frame_performance_summaryOverall frame health metrics"Summarize frame performance and report jank rate and P99 CPU time"

🔒 Concurrency & IPC

ToolPurposeExample Prompt
thread_contention_analyzerFind synchronization bottlenecks"Find lock contention between 15s–30s and show worst waits"
binder_transaction_profilerAnalyze Binder IPC performance"Profile slow Binder transactions and group by server process"

💾 Memory Analysis

ToolPurposeExample Prompt
memory_leak_detectorFind sustained memory growth patterns"Detect memory-leak signals over the last 60s"
heap_dominator_tree_analyzerIdentify memory-hogging classes"Analyze heap dominator classes and list top offenders"

Output Format

All tools return structured JSON with:

  • Summary: High-level findings
  • Details: Tool-specific results
  • Metadata: Execution context and any fallbacks used

📚 Resources

📄 License

Apache 2.0 License. See LICENSE for details.

GitHub • Issues • Documentation

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