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

The readOnlyHint already tells the agent this is safe to call, and the schema/output schema cover the data shape. The description adds an interpretive behavioral cue ('a rising negative share usually warrants drilling') that is useful but doesn't disclose additional mechanics such as aggregation scope or latency; with annotations covering safety, this is adequate but not rich.

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?

Two sentences front-load the output shape, then state the optional filter and the business use case. There is no filler and every sentence contributes to selection or invocation.

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?

Together with the 100%-covered schema, output schema, and read-only annotation, the description is essentially complete for calling this tool. It would be slightly stronger if it explicitly distinguished itself from get_answer_type_distribution, but that is a minor gap given the clarity of 'sentiment' as opposed to 'answer type'.

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 parameters are already fully documented. The description only restates the optionality of answer_types ('Optionally restrict to specific answer types') and adds nothing about time_filter; baseline 3 is appropriate because the schema carries that 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 clearly identifies the tool as returning a sentiment distribution ('counts of positive, negative, neutral') over chat answers, so an agent knows what data to expect. It lacks an explicit verb like 'returns' and does not call out the similarly named sibling get_answer_type_distribution, but the sentiment-vs-type distinction is inferable from the description.

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 a concrete use case ('quick quality signal') and an explicit follow-up path ('drilling into individual sessions via read_sessions') when negative sentiment rises. It doesn't enumerate exclusions or compare with get_answer_type_distribution, but the guidance is actionable and specific.

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