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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint). The description adds genuinely new behavioral context: every line carries a named public source per jurisdiction (UK bodies, TTB for US, HSE for hangover), it is explicitly not medical or legal advice, and it will state when 0.0% is the better choice. It does not describe answer length or how a no-answer case is handled, 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?

The purpose is front-loaded in the first clause, which is good. However, the bulk of the description is a parenthetical-heavy run-on that re-enumerates all 29 enum values already present in the schema, and the useful glosses (labels, units, price, best_buy) are buried mid-list where they are easy to miss.

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 single-enum, read-only lookup tool with no output schema, the description supplies what matters: subject domain, sourcing policy, jurisdictional caveats, advice disclaimer and topic coverage. Return shape need not be explained without an output schema, so only the missing guidance on when to defer to siblings is a real gap.

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 0%, so the description carries the burden, and it meaningfully disambiguates several opaque enum values: labels (alcohol-free/0.0%/de-alcoholised/0.5%), units (alcohol in 0.5%), everyday_foods (trace alcohol in juice and bread), alcohol_testing (EtG for work, licences, recovery), price (duty, why not cheaper) and best_buy (which beer to buy). Several values (sleep, gout, diabetes, liver, vegan) are listed with no gloss, but the names are largely self-explanatory.

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: answers common UK questions about alcohol-free drinks, with the distinguishing scope qualifier 'in general' and a named-source guarantee. This implicitly separates it from brand/order-centric siblings such as ask_impossibrew, browse_beers and compare_beers, but it never names an alternative explicitly, so it stops short of a 5.

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 only implied by the framing 'answers common UK questions' and the 'Not medical or legal advice' caveat. There is no explicit when-to-use (e.g. 'for general reference questions, not product or order questions') and no routing to ask_impossibrew or compare_beers for brand-specific or purchase intent.

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