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

Beyond the readOnly/openWorld/idempotent annotations, it discloses rate limiting (over the source's 12 calls/minute yields a free 503 with Retry-After), error economics (400 for bad input is free, data-source failure not charged), and the billing model ($0.16 per successful call over x402 USDC on Base, payment in _meta["x402/payment"]). That is exactly the operational context an agent needs before invoking a paid, rate-limited call.

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?

Purpose, scope and return fields are front-loaded in the first clause, with operational caveats (rate limits, error costs, price, payment) following. It is dense but nearly every clause carries information; the payment mechanics sentence is long yet necessary for a paid tool.

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?

There is no output schema, so the description carries the burden of describing results and does so: ideas count, sort order, and the returned metrics including optional monthly history. Combined with disclosed rate limits, failure modes and pricing, an agent has everything needed to call it correctly and predict the cost.

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%, and the schema already documents limit, country vs locationCode precedence, languageCode fallbacks, and the missing-keyword behavior. The description restates the headline bounds (20 seeds, 100 ideas, optional monthly history) but adds no format or edge-case meaning beyond the schema, so the baseline of 3 applies.

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 names a specific verb+resource (Google Ads keyword ideas) and quantifies scope precisely: up to 20 seed keywords, up to 100 ideas, sorted by highest search volume, returning monthly volume, CPC, competition and top-of-page bid range across 94 countries. Naming the data source as 'Google's own planner' lets an agent distinguish it from nearby siblings such as keyword_ideas, related_keywords or keyword_variations without opening a schema.

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?

'Use when planning a paid or SEO keyword list from Google's own planner' gives a clear selection context tied to the paid-planner data source. It stops short of explicitly naming when-not to use it or pointing at a specific alternative sibling, so routing among the many keyword_* tools still relies partly on inference.

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