Perfetto MCP

Perfetto MCP turns the complexity of Android performance tracing into a conversational experience. Instead of manually writing SQL against Perfetto’s trace database, developers can ask questions in plain English—“Why is the FragmentManager taking 438 ms?” or “Show me any ANR events”—and receive precise, context‑aware answers. This eliminates a major friction point in mobile performance debugging: the steep learning curve of Perfetto’s query language and the overhead of parsing raw trace data.

At its core, the server receives a natural‑language prompt, automatically translates it into an optimized Perfetto SQL query, and executes that query against the specified trace file. The results are then formatted back into a concise explanation or visual summary that can be displayed directly in an AI assistant or IDE. By handling the translation and execution pipeline, Perfetto MCP frees developers from boilerplate code and lets them focus on interpreting findings rather than engineering queries.

Key capabilities include:

• ANR detection: The server automatically flags Application Not Responding events, providing a clear timeline and root‑cause hints.
• Performance profiling: CPU usage, frame jank, and memory leaks are surfaced with minimal effort.
• Thread contention analysis: Lock acquisition patterns and potential bottlenecks are identified in seconds.
• Binder profiling: Inter‑process communication delays and slow system calls are highlighted, aiding in IPC optimization.
• Natural language to SQL: Every query is generated on the fly, ensuring that the analysis stays tightly coupled with the user’s intent.

In real‑world scenarios, Perfetto MCP shines during nightly build validation, regression testing, or on‑device diagnostics. A QA engineer can ask an AI assistant to “Show me any frame drops over 16 ms in the latest trace” and instantly receive actionable insights. During a production incident, an engineer can quickly surface ANR traces without digging through logcat or manually parsing trace files. Integrations with tools like Claude, VS Code, and Codex mean that the same conversational workflow can be embedded in code reviews, pair programming sessions, or automated monitoring dashboards.

What sets Perfetto MCP apart is its tight coupling between the AI’s natural‑language interface and the low‑level Perfetto trace engine. Developers gain a powerful, reusable analysis layer that scales from single‑device debugging to large‑scale telemetry pipelines—all without writing a line of SQL.