KnappeKeuze

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

With no annotations at all, the description carries the full burden and does disclose meaningful behavior: the exact comparison dimensions, that a winner is indicated per score, and the important limitation that earbuds vs. headphones are only compared on sound, noise cancelling and microphone. It does not explicitly state it is a read-only, non-mutating operation, which would close the remaining gap.

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 with no filler, and the core action is front-loaded. The parenthetical exceptions make it slightly run-on, but every clause conveys comparison scope or a caveat that matters.

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?

There is no output schema, so the description must describe the return content, and it does: per-attribute winners, practical data with source, and prices with a timestamp. Combined with the fully documented parameter, an agent has enough to invoke it correctly, though an explicit read-only/mutation statement is absent.

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?

The single parameter has 100% schema coverage including an example array, and the description only restates 'Geef de modellen als naam of id' plus the 2-of-3 count already encoded in minItems/maxItems. It adds essentially no meaning beyond the schema, so the high-coverage baseline of 3 applies.

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 gives a specific verb and resource ('Zet 2 of 3 modellen ... naast elkaar' / compare 2-3 models) and details exactly what the comparison covers (scores per attribute, practical data with source, checked prices with timestamp). It never names the sibling search_products, so the distinction from it is left to inference.

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?

The implicit use case is clear (side-by-side comparison of 2-3 specific models rather than searching), but there is no explicit statement of when to prefer this over search_products or of prerequisites such as passing valid existing model names/ids. The min/max model count appears only through the schema constraints.

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

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

No annotations exist, so the description carries the full burden and it does substantial work: it discloses that scores come from a fixed formula with no AI, that specs carry a source, and that prices carry a check timestamp. It also imposes a real behavioral obligation ("Noem bij een prijs het tijdstip van de controle") that appears nowhere in the schema. Gaps remain around result-set behaviour such as whether prices are cached or how many results come back by default.

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?

Three sentences, front-loaded with the search scope and then the payload, with no filler. The final citation instruction is slightly buried at the end but is short enough not to bloat the definition.

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 five optional parameters and no output schema, the description usefully compensates by enumerating what comes back: score, subscores, sourced specs, timestamped price, and product link. Missing are result-count limits and defaults, which matter for an unfiltered search over a catalogue.

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% with two enums, so the schema already explains form, sort, limit, query and max_price in detail. The description adds no parameter-level meaning beyond that, which is the baseline 3 case.

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?

"Zoek koptelefoons en oordopjes in de catalogus van KnappeKeuze" gives a concrete verb plus resource and even names the catalogue, so the agent knows exactly what is being searched. It does not differentiate itself from the sibling compare_products, so an agent has to infer which one to pick.

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: the promise of per-model scores and prices suggests a discovery/browse use case, but there is no explicit when-to-use statement, no exclusion, and no pointer to compare_products. The agent must guess at the boundary between searching and comparing.

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