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

Annotations already declare this a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is lower. The description nevertheless adds a genuinely non-obvious output behavior: the positivity percentage is null when there are fewer than 3 answers, which prevents an agent from misreading a null as an error or a zero. It also previews that a real negative excerpt and per-engine comparison are included, which shapes how the result should be interpreted.

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 a single dense sentence, front-loaded with the core framing 'How AI answers speak about the brand' before the metric list. Every clause carries substance—no filler or restatement of the title—though the comma-chained enumeration would be easier to parse as a short bulleted list.

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?

With no output schema, the description bears the burden of telling the agent what comes back, and it does so thoroughly, including the small-sample null rule. Parameter behavior is fully covered by the schema. The remaining gap is routing guidance against sibling analytics tools, which no other field supplies.

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%; every parameter (from, to, brand, limit, engine) is documented with defaults and formats in the schema itself. The description contributes nothing about parameter usage—for example, it never explains how the engine filter interacts with the per-engine breakdown it advertises. Baseline 3 under full schema coverage.

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 resource (brand sentiment inside AI answers) and enumerates the exact metrics returned: positivity percentage, the positive/neutral/negative split with a negative excerpt, per-engine breakdown, competitor comparison, and negatively-toned questions. That is far more specific than the tautological title 'Get sentiment'. It stops short of distinguishing itself from siblings like get_visibility or get_analytics, which plausibly overlap on brand-in-AI-answers data.

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?

There is no statement of when to reach for this tool versus get_visibility, get_analytics, or search_responses, and no prerequisites or exclusions are given. The only usage signal is implicit in the metric list, leaving the agent to infer that this is the tone-focused complement to a visibility tool.

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