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

Annotations already mark the tool read-only and idempotent, so the description supplements rather than repeats them. It discloses the 0-100 value scale, that absolute volume appears when available, that growth windows are fixed presets, and the plan-limit behavior on throttling—useful behavioral context beyond annotations.

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

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

The description is front-loaded with the core capability and each subsequent sentence adds distinct operational value (value scale, sibling choice, source caveat, rate-limit behavior). It is tight for the amount of guidance it delivers.

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

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

With output schema present, return-value details do not need to be in the description. The description covers sibling differentiation, source/keyword caveats, value semantics, and error handling, so an agent has all behavioral context needed to invoke correctly.

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

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

Schema description coverage is 100%, so the schema already documents source values, keyword formats, and percent_growth presets fully. The description mostly restates these constraints (preset strings, Android bundle ID) rather than adding new parameter-level details; the 0-100 scale is output behavior, not parameter semantics.

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

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

The description opens with a specific, verb-driven statement: 'Point-to-point growth for a keyword on one or more sources.' It also disambiguates from siblings by saying to prefer this over get_time_series for growth questions and clarifying that app sources here are not the App Store/Google Play live boards on get_top_trends.

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

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

It gives explicit routing guidance: 'Prefer this over get_time_series for growth questions' and explicitly carves out get_top_trends for app-store live boards. It also adds an operational rule for rate-limit/quota errors, telling the agent to inform the user their plan limit is reached.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context beyond those: the series is bounded to '0-100 values,' volume is included 'when available,' and rate-limit/quota failures should be reported as a plan limit issue. This meaningfully supplements the annotations.

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

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

Four short sentences, each earning its place: the core behavior, intended uses, exclusions with alternatives, and failure handling. The most important information is front-loaded, and there is no redundant restatement of the schema or annotations.

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

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

For a two-parameter historical data tool with a rich schema and an output schema, the description covers purpose, usage boundaries, alternatives, and edge behavior. Nothing an agent needs to select and invoke this tool correctly is missing.

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

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

The input schema already provides 100% parameter coverage with detailed descriptions for both keyword and source, including per-source keyword format rules and valid source values. The description adds only the high-level constraint 'one keyword and one source,' which the schema already implies. Baseline 3 is appropriate since the schema carries the parameter documentation burden.

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

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

The description states the exact resource and scope: 'Full historical series for one keyword and one source (0-100 values, plus volume when available).' It also names the intended use cases ('charting or custom math') and distinguishes itself from siblings by explicitly naming get_top_trends and get_growth.

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

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

Usage guidance is explicit and actionable: use for charting/custom math, not for live trending boards ('use get_top_trends'), and for most growth questions 'use get_growth.' It even includes rate-limit/quota handling behavior, leaving no ambiguity about when this tool is appropriate.

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

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

Beyond the annotations (read-only, open-world, idempotent), the description discloses the live board nature, that these are not keyword lookups, default sort behavior, and the rate-limit/quota response instruction telling the user their plan limit is reached. It also clarifies the live-vs-history distinction. No contradictions.

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

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

The description is compact and front-loads the core purpose in the first sentence. Each subsequent sentence adds a distinct piece of guidance—category, sort, alternatives, rate-limit handling—without filler.

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

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

The tool is fully covered: purpose, category requirements, sort/window semantics, alternatives, and error handling are all present. An output schema exists, so return values don't need describing. Minor schema details like 'Only omit on a first pull' are already captured in the input schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description repeats sort/category guidance already present in the schema, such as 'Use sort='rank_change'' and 'always pass category,' without adding new parameter-specific meaning. The extra context like 'No keyword' and 'live store boards' is about tool behavior, not parameter semantics.

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

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

The description states the tool provides a live top-trending board for exactly one feed type and explicitly notes 'No keyword.' It distinguishes itself by pointing to get_growth/get_time_series for app history, so an agent can tell when to use this tool. The purpose is specific and resource-focused.

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

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

The description gives explicit when-to-use guidance: use get_growth or get_time_series for an app's history, and 'Do not use get_time_series for live boards.' It also spells out when category must be passed for specific feed types. This is far beyond a vague statement of scope.

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