KitchenWatt (US)

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorldHint=false, so safety is covered. The description adds that comparison happens 'on the same criteria,' hinting at a uniform result shape, but says nothing about behavior with invalid, duplicate, or cross-market slugs, or whether the response is empty/partial in those cases.

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?

A single sentence with no filler, front-loaded with the verb and the cardinality constraint. Slightly dense with the parenthetical, but nothing is wasted.

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 read-only two-parameter comparison tool with full annotation coverage and no output schema, the definition supplies enough to select and call it. The remaining gap is the unstated nature of 'the same criteria' and slug-validity expectations, which are minor.

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 50%: market is fully documented in the schema (us/fr catalogs), while slugs has no schema description. The description partially compensates by identifying slugs as KitchenWatt comparison slugs in a 2-to-4 group, but does not address uniqueness, market consistency across slugs, or what a malformed slug does.

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?

Specific verb and resource: 'Compare 2 to 4 food processors ... side by side on the same criteria,' which also names the domain (food processors) and the source of the identifiers (KitchenWatt's comparison slugs). The 2-to-4 scope implicitly separates it from single-item siblings like get_model, though it never names an alternative explicitly.

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 'compare' verb and the 2-to-4 bound, and the parenthetical points at where slugs come from. There is no statement of when to prefer this over search_models or get_model, nor any precondition such as needing valid slugs from a prior lookup.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds value beyond that by disclosing the shape of the response (four named content sections), which matters because no output schema exists. It does not cover failure behavior for unknown ids or the effect of switching markets.

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?

A single sentence with the core action front-loaded and no filler. The trailing enumeration of returned sections is a bit list-like and the phrasing ('our data ... its official data') is slightly muddled, but nothing is wasted.

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 only two parameters, no output schema, and a closed-world read, the description covers the essentials of what is returned and where the id comes from. It is incomplete for the market parameter's behavioral implications and gives no basis for choosing this tool over the near-identical get_model sibling.

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 50%: market is fully documented in the schema (enum us/fr with catalog and language notes), while id has no schema description. The description compensates partially by explaining that the id comes from search, but it never mentions market at all, even though market selects a distinct catalog and affects results.

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 clear verb and resource — retrieving the full content of a single KitchenWatt model identified by an id — and even enumerates the returned sections (our data, filter values, official data, address). It never distinguishes this from the sibling get_model, which appears to be the same operation, so an agent cannot confidently pick between them from the text alone.

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 parenthetical 'an id returned by search' gives useful implied guidance about where the required argument comes from. There is no explicit when-to-use/when-not guidance and no mention of how this relates to get_model, search, or search_models, leaving the selection decision to inference.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered. The description adds value by saying the response includes all model data plus its page link, which matters since there is no output schema. It does not say what happens when the slug is invalid or whether slugs are market-scoped.

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?

One sentence with no padding, and the identity mechanism (slug) is stated inline. The phrasing 'All the data of one of the food processors in KitchenWatt's comparison' is slightly convoluted but still front-loads the essential 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?

For a simple two-parameter read tool with rich annotations, the main remaining gaps are return-shape detail and error behavior for unknown slugs, which the description only partially covers by mentioning the page link. It is adequate but not complete.

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 50%: the market parameter is thoroughly documented in the schema, including enum meanings and the separate-catalog caveat, while slug has no schema description. The phrase '(by its slug)' only minimally compensates, leaving slug format and provenance (presumably from search_models) unexplained.

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+resource: fetch the full data of a single food processor model identified by its slug, plus the link to its page. That clearly separates it from sibling tools like search_models or compare_models. It is only slightly muddied by the narrow phrasing 'food processors,' which may understate the catalog's scope.

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 rather than stated: a single-item lookup keyed by slug, so an agent can infer it is used after a search has produced a slug. No explicit when-to-use or when-not-to-use guidance is given, and no sibling alternative is named for cases where the slug is unknown.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is clear. The description adds value by describing the returned filter metadata, but it does not mention behavior such as market-specific catalogs, result shape, or pagination.

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 definition is a single front-loaded sentence that identifies the resource and lists the relevant return fields without unnecessary wording. Every phrase contributes to explaining what the filter data contains.

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 simple read-only listing tool with one fully documented parameter and explicit annotations, the description provides enough context about the returned filter fields. It could be stronger with a brief note connecting the tool to filter-based search or comparison workflows, but nothing critical is missing.

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%, and the schema fully explains the single `market` parameter, including the difference between the US and French catalogs. The description adds no additional meaning beyond the schema, so the baseline score 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 clearly identifies the resource as the filters for KitchenWatt's food processors comparison and enumerates the metadata each filter contains (key, label, possible values/range, unit, meaning). It is specific enough to distinguish this from search or comparison tools, though it does not explicitly contrast itself with sibling tools or state a verb.

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 description states what the tool returns but gives no guidance on when to use it, when not to use it, or which sibling tool to choose instead. Implicit usage can be inferred, but no explicit discovery or prerequisite context is provided.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds only "with their address", a fragmentary hint at return shape, and says nothing about ordering, result count, or what a guide record otherwise contains.

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?

One front-loaded sentence with no padding. The trailing "with their address" is awkwardly phrased and slightly ambiguous (URL vs. physical address), which keeps it from a 5.

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 is the only place an agent can learn what a guide record looks like, and "address" is the sole field hinted at. For a listing endpoint, the absence of any statement about result volume, ordering, or record fields leaves 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 coverage is 100% and the single enum parameter is fully documented in the schema, including what each market implies for language and catalog. The description contributes nothing about the market parameter, so the baseline 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?

"List" + "buying guides" is a specific verb and resource, and "for food processors" bounds the scope, so the agent knows what comes back. It never mentions the sibling tools (search, fetch, get_model, compare_models), so it does not help an agent distinguish this listing tool from the rest of the catalog.

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 when-to-use statement, no condition under which an agent should prefer list_guides over search or fetch, and no prerequisites or exclusions. The agent must infer usage entirely from the name.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds that each market has its own catalog and that results carry id, title and address, but says nothing about result limits, pagination, or empty-result behavior.

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?

A single front-loaded sentence that packs purpose, scope, and return fields with no filler. The parenthetical market detail is dense but each clause carries information an agent needs.

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 two-parameter read-only search with no output schema, the description covers the resource, the market/language split, and the shape of returned fields. The remaining omission is behavioral detail on result volume and paging, which is minor but not nothing.

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 only 50% because the required 'query' parameter has no schema description; the description compensates by defining it as free text. It also restates the market mapping (us = Amazon.com/English, fr = Amazon.fr/French), which adds useful reinforcement of the enum's meaning.

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 (search) and a specific resource (food processors in KitchenWatt's comparison) scoped to two named markets, so an agent knows exactly what is being searched. It does not, however, differentiate itself from the sibling search_models, which leaves real ambiguity about which search tool 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?

The description explains what the tool searches and that the query is free text, which implies keyword-style usage, but it never states when to choose this over search_models or compare_models, nor any exclusions or prerequisites. With a near-identical sibling name, the absence of routing guidance is a meaningful gap.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile needs no restating. The description does add real value by disclosing that results carry official data with its source and that today's price and ratings live on the linked page rather than in the response, which prevents the agent from expecting price fields. It still says nothing about result ordering, pagination beyond the limit cap, or what an empty match looks like.

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 front-loaded sentence that leads with the verb and domain, then market scope, then input modes, then return shape, with no filler. The parenthetical about price and ratings earns its place. Density is high but nothing is redundant.

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 carries the return-value burden and does so adequately by enumerating the fields returned and pointing to the page for price and ratings. The main remaining gap is the nested filters object, whose key vocabulary and provenance are not explained, which matters more than the untouched limit default. For a read-only search tool with rich annotations, it is close to complete.

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 75%, so baseline is 3. The description reinforces the free-text intent of query and the exactness requirement of filters, but the market semantics it states are already spelled out in the enum description, and limit is never mentioned. Critically, for the nested filters object it does not explain where valid keys and values come from (presumably list_filters), leaving the hardest parameter under-specified.

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 verb and resource ('Search the food processors in KitchenWatt's comparison') and scopes it to two named markets, so the agent knows exactly what domain it queries. It also previews the returned fields (name, brand, filter values, official data with source, page link). It never contrasts itself with siblings like compare_models, get_model, or the generic search, so differentiation 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?

Usage is implied rather than stated: the phrase 'by need (free text) and exact filters' suggests combining a natural-language query with structured constraints, and the market sentence hints that each catalog is separate. There is no explicit when-to-use or when-not-to-use guidance, and no routing to alternatives such as list_filters for discovering valid filter keys or get_model for detail on a single result.

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