AI mention sentiment breakdown

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, so the safety profile is covered. The description adds a real behavioral constraint beyond them: dates must fall within the data retention window. It does not disclose pagination or result-size behavior, keeping it short of a 5.

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?

Front-loads the returned payload, then alternatives, then the date constraint, then example questions. Every sentence carries distinct information with no filler or repetition.

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?

No output schema exists, but the description conveys the shape of the result (positive/neutral/negative split, per engine, per competitor) and the date constraint. Gaps remain on the four undocumented parameters and on what empty engines/countries arrays mean, but nothing essential to correct invocation 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?

Schema coverage is only 38% across 8 parameters; dateFrom/dateTo, organizationId and projectId have no schema descriptions. The description partially compensates by tying dates to the retention window and naming the per-engine/per-competitor dimensions, but it says nothing about engines, countries, queryClusterIds or includeUngroupedQueries defaults.

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?

States a specific verb+resource (sentiment split of the brand's AI mentions) and scopes it precisely to a date window, broken down per AI engine and per competitor. It also explicitly names the siblings it is not (get_mention_mix for counts, get_mention_samples for texts), so an agent can route correctly without opening schemas.

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?

Explicitly gives when to use it ('tone/sentiment questions') plus two named alternatives with the exact conditions that select them: get_mention_mix for mention counts by type/tone/qualifier, get_mention_samples for raw texts. It even supplies example questions to anchor intent.

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