tooluse-labs/perfetto-mcp-rs
by Various
Perfetto trace analysis via MCP — PerfettoSQL queries plus dedicated Chrome tools for scroll jank, page loads, and main-thread hotspots
MCP
tooluse-labs/perfetto-mcp-rs
Added 1 June 2026
Overview
This tool extends the Model Context Protocol (MCP) to let LLMs query Perfetto traces using PerfettoSQL. It also provides dedicated Chrome tooling for analyzing scroll jank, page load performance, and main-thread hotspots.
Best for
Best for
Performance engineers and developers who want to analyze Chrome traces using natural language or LLM-driven workflows
Use cases
- Debug rendering jank from Chrome traces via natural language queries
- Automate trace analysis for page load performance regressions
- Identify main-thread CPU hotspots without reading raw trace files
How to use
Install
curl -fsSL https://raw.githubusercontent.com/tooluse-labs/perfetto-mcp-rs/main/install.sh | sh Tools exposed
load_traceexecute_sqllist_tableslist_table_structurelist_processeslist_threads_in_processslice_descendants_breakdownlist_stdlib_moduleschrome_scroll_jank_summarychrome_page_load_summarychrome_page_load_resource_summarychrome_page_load_resource_pipelinechrome_page_load_resource_hotspotschrome_page_load_script_hotspotschrome_main_thread_hotspotschrome_startup_summarychrome_web_content_interactions
Tested with
Claude Code, Codex, Claude Desktop, Cursor
Notes
This tool extends the Model Context Protocol (MCP) to let LLMs query Perfetto traces using PerfettoSQL. It also provides dedicated Chrome tooling for analyzing scroll jank, page load performance, and main-thread hotspots.
15 stars on GitHub. Last updated 2026-05-27. Licensed Apache-2.0.
Use cases
- Debug rendering jank from Chrome traces via natural language queries
- Automate trace analysis for page load performance regressions
- Identify main-thread CPU hotspots without reading raw trace files
Pros
- Bridges LLM and Perfetto for interactive trace exploration
- Specialized Chrome performance queries reduce manual analysis time
- Written in Rust for efficient trace processing
Cons
- Small user base limits community support and examples
- Requires familiarity with MCP and Perfetto trace collection
- Rust dependencies may add build complexity for some environments
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Bridges LLM and Perfetto for interactive trace exploration
- Specialized Chrome performance queries reduce manual analysis time
- Written in Rust for efficient trace processing
Cons
- Small user base limits community support and examples
- Requires familiarity with MCP and Perfetto trace collection
- Rust dependencies may add build complexity for some environments
Pairs with
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