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chrome_scroll_jank_summary

Read-onlyIdempotent

Summarizes the most janky scroll frames in a Chrome trace, listing cause, delay, and scroll identifiers. Helps investigate jank reports and find scroll regressions.

Instructions

Summarize the worst scroll jank frames in a Chrome trace: cause_of_jank, sub_cause_of_jank, delay_since_last_frame, event_latency_id, scroll_id, vsync_interval. One row per janky frame, sorted by delay_since_last_frame DESC. Read-only.

Use when: investigating jank reports, finding scroll regressions, ranking jank causes. Prefer over hand-rolling SQL on chrome.scroll_jank.scroll_jank_v3 — same data, less code.

Don't use for: non-Chrome traces (will error). For custom filters, use execute_sql against the same view.

Parameters: optional limit (default 100, capped at 5000) and max_string_len. Operates on the loaded trace.

Output: metadata-first JSON; row_count exact; truncated=true means more rows exist; string_truncated=true means shortened text.

Empty result: no janky frames detected (clean trace) or no scrolls occurred during capture.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoOptional max rows to return. Defaults to 100 and is capped at 5000. Must be > 0 when set; accepts both numbers and numeric strings.
max_string_lenNoOptional per-string-cell character cap applied to returned Chrome-tool rows only. Unset preserves full strings for precision; accepts both numbers and numeric strings. Must be > 0 when set.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint, destructiveHint, idempotentHint), the description adds: read-only confirmation, operates on loaded trace, output metadata (row_count, truncated flags), and empty result interpretation. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Eight concise sentences covering purpose, usage, parameters, output, and edge cases. Front-loaded with key information, no unnecessary words, well-structured with clear sections.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all essential aspects: purpose, usage, parameters, output format (including metadata fields like row_count, truncated), empty result explanation. Without an output schema, the description still provides complete guidance for the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Though schema coverage is 100%, the description adds crucial context: limit defaults to 100 (schema says null) and is capped at 5000, max_string_len truncates strings. It explains output impact of these parameters beyond schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool summarizes worst scroll jank frames in a Chrome trace, listing specific output columns (cause_of_jank, delay_since_last_frame, etc.) and sorting order. It clearly distinguishes from sibling tools by focusing on scroll jank.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases (investigating jank reports, finding regressions), a preferred alternative (hand-rolling SQL), and clear exclusions (non-Chrome traces will error) with a specific alternative for custom filters (execute_sql).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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