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

📋 Prerequisites

• Python 3.13+ (macOS/Homebrew):

  brew install python@3.13
  

• uv (recommended):

  brew install uv
  

🚀 Getting Started

IDE Integration

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

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

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

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

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

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.

Edit ~/.codex/config.toml:

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

Local Install

uvx perfetto-mcp

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:

Parameter	Description	Example
trace_path	Absolute path to your Perfetto trace	/path/to/trace.perfetto-trace
process_name	Target process/app name	com.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

Tool	Purpose	Example Prompt
find_slices	Survey slice names and locate hot paths	"Find slice names containing 'Choreographer' and show top examples"
execute_sql_query	Run 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

Tool	Purpose	Example Prompt
detect_anrs	Find ANR events with severity classification	"Detect ANRs in the first 10s and summarize severity"
anr_root_cause_analyzer	Deep-dive ANR causes with ranked likelihood	"Analyze ANR root cause around 20,000 ms and rank likely causes"

🎯 Performance Profiling

Tool	Purpose	Example Prompt
cpu_utilization_profiler	Thread-level CPU usage and scheduling	"Profile CPU usage by thread and flag the hottest threads"
main_thread_hotspot_slices	Find longest-running main thread operations	"List main-thread hotspots >50 ms during 10s–25s"

📱 UI Performance

Tool	Purpose	Example Prompt
detect_jank_frames	Identify frames missing deadlines	"Find janky frames above 16.67 ms and list the worst 20"
frame_performance_summary	Overall frame health metrics	"Summarize frame performance and report jank rate and P99 CPU time"

🔒 Concurrency & IPC

Tool	Purpose	Example Prompt
thread_contention_analyzer	Find synchronization bottlenecks	"Find lock contention between 15s–30s and show worst waits"
binder_transaction_profiler	Analyze Binder IPC performance	"Profile slow Binder transactions and group by server process"

💾 Memory Analysis

Tool	Purpose	Example Prompt
memory_leak_detector	Find sustained memory growth patterns	"Detect memory-leak signals over the last 60s"
heap_dominator_tree_analyzer	Identify 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

• Trace Processor Python API - Perfetto's Python interface
• Perfetto SQL Syntax - SQL reference for custom queries

📄 License

Apache 2.0 License. See LICENSE for details.

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