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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so safety is covered. The description adds real behavioral context beyond that: the return shape (week sentence + up to 5 ideas), the per-idea evidence trail (cited ad ids), and that `open` is the default status while acted-on/dismissed ideas are opt-in. It does not discuss volume limits or cost of the 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?

The core purpose is front-loaded in the first clause, and the example questions are placed last as routing cues. The middle parenthetical list of idea facets is dense but earns its place by telling the agent what evidence categories exist; still, it is one very long sentence that could be split for scannability.

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?

An output schema exists, so the lengthy return-value enumeration is partly redundant, but it usefully conveys semantics (evidence ad ids, confidence, suggested action, acted-on flag) that help the agent frame a follow-up. Combined with annotation coverage and documented defaults, an agent has enough to call this correctly; only cross-parameter interaction and result volume remain unstated.

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%, with defaults and enums documented inline for weeks, status and language, so the schema carries the parameter burden. The description only adds one piece of semantics — that `id` narrows the response to a single idea — and does not clarify how weeks interacts with status or how language affects the idea content itself.

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 a concrete verb and resource — a weekly synthesized read of competitors producing a one-sentence week summary plus up to 5 actionable ideas — and enumerates the idea dimensions (offer, price, long runners, format, channel, hook, audience, language, gaps, strengths). This distinguishes it from sibling raw-data tools like get_competitor_ads or get_market_trends, which would return evidence rather than recommendations.

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 supplies two concrete trigger questions ('what should I do about my competitors?', 'what changed in the market this week?') that make the intended moment of use obvious, plus an explicit single-item lookup mode via `id`. It stops short of naming an alternative tool or stating when not to use it (e.g. when raw ad evidence from get_competitor_ads is wanted).

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