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get_vitals_freshness

Check vitals data freshness before querying to pick an end_date that returns crash, ANR, and error metrics, handling metric sets with no data gracefully.

Instructions

Check how fresh Android vitals data is before querying it. Returns each requested metric set with freshnessInfo.freshnesses[], giving the latest end time available per aggregation period (DAILY, HOURLY) — use it to pick an end_date the query_* tools will actually return rows for, since vitals data typically lags about a day. Defaults to the crash, ANR and error count metric sets. A metric set with no data for this app (slowRenderingRateMetricSet is games-only, for example) comes back with an error field instead of freshnessInfo rather than failing the whole call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metric_setsNoMetric sets to check (default: crashRateMetricSet, anrRateMetricSet, errorCountMetricSet)
package_nameNoApp package name, e.g. 'com.acme.app' (defaults to GOOGLE_PLAY_PACKAGE_NAME)
Install Server

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the default metric sets, the return structure (freshnessInfo.freshnesses[]), the lag (about a day), and the error handling for missing metric sets. However, it does not describe the exact format of the error field or whether the tool can fail entirely, nor does it explain the top-level structure of the response (e.g., does it return a single object or a list?). It adds significant context beyond schema but lacks some detail on failure modes and response format.

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

Conciseness4/5

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

The description is a single paragraph of moderate length, but it is dense with useful information. It front-loads the primary purpose ('Check how fresh Android vitals data is'), then lists key details (defaults, lag, error handling). It avoids unnecessary wordsholiness and stays focused. A slight point is lost for being a bit long and not broken into bullet points, but it earns its length.

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?

Given the complexity of the vitals data ecosystem festooned with many query tools, this description is comprehensive. It explains the tool's role, output structure, default behavior, and error handling, which is sufficient for the agent to use it effectively. There is no output schema, so the description must explain what is returned, and it does. It also addresses edge cases like apps without certain metric sets. This is complete for the tool's scope.

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

Parameters3/5

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

The schema covers 100% of parameters, including descriptions for both 'metric_sets' and 'package_name'. The description adds clarity about the default metric sets and the purpose of the tool, but does not add new syntax or format details for the parameters beyond what the schema already provides. For example, it doesn't specify the exact string values for the enums, but that's in the schema. Thus, baseline 3 is appropriate.

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 clearly states the tool's purpose: to check the freshness of Android vitals data before querying. It specifies the verb 'check', the resource 'Android vitals data', and the specific output 'freshnessInfo.freshnesses[]'. It distinguishes itself from the query_* siblings by explicitly positioning it as a preparatory step to pick valid end_date values.

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?

The description explicitly states when to use this tool: 'before querying it', and gives a clear use case: 'use it to pick an end_date the query_* tools will actually return rows for'. It also explains the behavior when a metric set has no data (returns an error field instead of failing), guiding the agent to handle that case. It does not explicitly name alternatives, but the context of sibling query_* tools is clear, and it implicitly tells the agent to call this before those.

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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