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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds valuable context by naming what comes back: questions to ask, claim checks, figure groups, and company cases grouped by kind of work. It stops short of describing format or how the caller should use the returned groups, which keeps it at 4.

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?

Purpose and examples are front-loaded and the sibling routing is last, which is the right order. The middle sentence listing four return components is dense and slightly run-on, but every clause carries information.

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 input schema and no output schema, the description carries the burden of explaining returns, and it does so by enumerating the four result categories. Minor gap: it does not say how the caller should match an incoming claim against the returned groups.

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?

Zero parameters, so the baseline is 4. The description correctly implies this is a reference/checklist lookup rather than a claim submitted for evaluation, matching the empty input schema.

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 ('check a claim made by an AI vendor or consultant') and immediately grounds it with three concrete example claims. It is clearly differentiated from the sibling check_ai_figure, which handles press/report statistics.

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?

Gives an explicit trigger ('Use it whenever someone selling or proposing AI quotes a result') and an explicit exclusion with the correct alternative ('For a statistic from the press or a report, use check_ai_figure'). Nothing is left to inference.

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