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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behaviour beyond that: the four-value answer vocabulary (yes/conditional/no/unknown), the assessment dimensions (availability, licence, contract, regulatory) and sources, plus a 'Not legal advice' caveat. It still omits freshness/dating of the underlying data and confidence handling for the 'unknown' outcome.

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?

Compact and front-loaded: the core question comes first, followed by the return shape and then a short disclaimer. The only real waste is the product-name prefix and the run-on first clause, which pack several qualifiers into one dense sentence.

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?

For a nine-parameter, no-output-schema tool, the description does well to enumerate the return values and the dimensions behind them. It falls short by not explaining how the optional qualifiers (sector, revenue band, audience, decision impact, data sensitivity) alter the outcome, which is exactly the information an agent needs to decide whether to supply them.

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 78% with eight enums, so the baseline is 3. The description does add meaning by glossing route values as direct API, third-party provider or open weights, and expanding jurisdiction codes to US, Canada, EU, UK, but it says nothing about the non-required inputs (sector, revenue, data, audience, decision) that materially change the verdict.

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 states a specific evaluation: whether a given model may be used, parameterised by purpose, route and jurisdiction, and it names the four-value answer scale. The branded 'Kingy Can I Use:' prefix is slightly noisy, and it never names a sibling such as can_i_run_locally, which is the nearest overlap (open weights vs local execution).

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?

Usage is implied by the framing ('whether a specific model can be used for a purpose, through a route, in a jurisdiction'), so an agent can infer this is the licensing/availability lookup. However there is no explicit when-to-use versus when-not guidance, no mention of when to prefer can_i_run_locally or get_model_prices, and no prerequisites stated.

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