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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered; the description adds genuinely useful behavior: the 0-100 scale with 50 as neutral, per-model breakdown, and a cap of up to 3 examples per label that are short excerpts. It omits latency, auth/prerequisite requirements, and what happens when data is sparse, which is the remaining gap.

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?

It is front-loaded with the purpose, then a use-case paragraph, then labeled 'Returns' and 'Example' blocks, which is easy to scan. The content earns its place, though the example question largely restates the earlier trigger phrases and could be trimmed.

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?

Despite having no output schema, the definition explains the return shape (overall score, per-model breakdown, capped excerpts), the scale semantics, and when to invoke it. For a read-only single-resource reporting tool with fully documented parameters, an agent has everything needed to call it 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 parameters like days, start_date, end_date, and brand_name are already fully documented, and the baseline of 3 applies. The description adds nothing about parameter behavior such as the timeframe window or the optional competitor brand_name, so it neither compensates nor subtracts.

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 opening sentence names a specific subject and resource: sentiment scores for a brand, overall and per model, with example excerpts. The trigger phrases ('how does AI describe us', 'what's the sentiment', 'is ChatGPT positive or negative') make clear this retrieves sentiment rather than visibility, share of voice, or raw answers, distinguishing it from siblings like get_visibility_score and get_ai_answers.

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 strong positive-trigger guidance with several concrete user phrasings and a worked example question, so an agent can recognize when to call it. It stops short of naming a when-not condition or an alternative sibling to use instead, which keeps it below a 5.

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