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get_wakeup_rate

Fetch excessive CPU wakeup rate data from Android Vitals to identify apps with frequent CPU wakeups that may trigger platform penalties.

Instructions

Fetch excessive CPU wakeup rate from Android Vitals.

Returns daily excessiveWakeupRate and distinctUsers by version code. Frequent CPU wakeups above platform thresholds may be penalized.

Args: package_name: Package name, e.g. com.example.myapp days: Past days to include (default 7, max 30). version_code: Optional version code filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
package_nameYes
daysNo
version_codeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The tool implementation for get_wakeup_rate, which calls the underlying ReportingClient to query for wakeup rate metrics and formats the response.
    def get_wakeup_rate(
        package_name: str,
        days: int = 7,
        version_code: str = "",
    ) -> str:
        """Fetch excessive CPU wakeup rate from Android Vitals.
    
        Returns daily excessiveWakeupRate and distinctUsers by version code.
        Frequent CPU wakeups above platform thresholds may be penalized.
    
        Args:
            package_name: Package name, e.g. com.example.myapp
            days: Past days to include (default 7, max 30).
            version_code: Optional version code filter.
        """
        days = max(1, min(days, 30))
        try:
            raw = _reporting().query_wakeup_rate(
                package_name=package_name,
                days=days,
                version_code=version_code or None,
            )
            rows = _parse_reporting_rows(raw.get("rows", []))
            if not rows:
                return json.dumps(
                    {
                        "packageName": package_name,
                        "message": (
                            "No excessive wakeup data available. Data may lag up to 2 days "
                            "or the app has no wakeup violations in this period."
                        ),
                        "rows": [],
                    },
                    indent=2,
                )
            return json.dumps(
                {
                    "packageName": package_name,
                    "periodDays": days,
                    "totalRows": len(rows),
                    "rows": rows,
                },
                indent=2,
            )
        except Exception as exc:
            return json.dumps({"success": False, "error": str(exc)}, indent=2)
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses read-only nature via 'Fetch', return granularity ('daily'), specific output fields ('excessiveWakeupRate', 'distinctUsers'), and domain significance (penalties). Lacks operational details like rate limits or pagination.

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?

Efficient 3-sentence preamble front-loaded with purpose and return value summary, followed by structured Args section. Every sentence earns its place; no repetition of tool name or tautology.

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

Completeness4/5

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

With output schema present, description appropriately summarizes key return fields rather than detailing full structure. Complete parameter documentation compensates for empty schema. Minor gap regarding API quota or rate limit context.

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?

Schema has 0% description coverage. Description fully compensates with semantics for all 3 parameters: package_name includes example format (com.example.myapp), days specifies valid range (max 30) and temporal meaning (past days), version_code clarifies filtering purpose.

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?

Specific verb 'Fetch' and clear resource 'excessive CPU wakeup rate from Android Vitals'. Explicitly distinguishes from siblings like get_wakelock_rate and get_crash_rate by specifying 'CPU wakeup' vs other vitals metrics.

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

Usage Guidelines3/5

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

Provides domain context that frequent wakeups 'may be penalized', implying when to use the tool (investigating penalty risks). Lacks explicit comparison to alternatives like get_vitals_summary or get_wakelock_rate.

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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