YardRider (US)

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 covered. The description usefully adds that models are compared 'on the same criteria' and that input is by comparison slug, but it says nothing about ordering, output shape, or what happens with duplicate or invalid slugs.

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 with no filler; the arity constraint appears early. The parenthetical '(slugs)' is slightly awkward, 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?

For a simple two-parameter read tool with full annotations, the description is adequate but thin. With no output schema, it could have said more about what the comparison result contains (criteria table, missing-model handling), which an agent would need to interpret the response.

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 fully documented in the schema with an enum and market explanation, while slugs relies on minItems/maxItems. The description adds a little meaning by tying slugs to YardRider's comparison slugs, but does not clarify slug format or whether duplicates are allowed, so it only marginally exceeds the schema baseline.

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 verb ('Compare') and resource ('lawn mowers from YardRider's comparison') with an explicit arity of 2 to 4. It is distinguishable from siblings like search_models or get_model, though it never names those alternatives directly.

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 2 to 4' constraint, which tells the agent the minimum and maximum number of models to supply. However, there is no explicit guidance on when to prefer this over search_models or get_model, and no mention of prerequisites such as needing valid slugs first.

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?

The annotations already declare read-only, idempotent, non-destructive behavior. The description adds useful return-content context that is absent from structured fields: it says the tool returns 'our data, its filter values, its official data and its address.' It still does not explain market-specific behavior, but the output composition is meaningfully disclosed.

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 description is a single front-loaded sentence with no filler. The wording is slightly awkward but remains efficient and readable.

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 fetch tool with two parameters and no output schema, the description covers the main purpose and return content. It is still incomplete because it does not clarify the distinction from get_model or explain market-specific fetching behavior beyond what the schema already states.

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%. The description compensates for the undocumented id parameter by saying it is an id returned by search, but it does not mention the market parameter or clarify how the market changes the catalog being fetched.

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 states a specific action and resource: fetching the full content of one YardRider lawn mower by id. It does not, however, differentiate this tool from the sibling get_model, which appears to serve a very similar purpose.

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 phrase 'an id returned by search' implies this should be used after a search, providing some usage context. It does not explicitly say when to use this instead of get_model or search_models, nor does it state any exclusions.

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 fully covered elsewhere. The description adds that the response contains 'all the data' plus a link to the model's page, which is useful since there is no output schema, but 'all the data' is vague and nothing is said about unknown-slug behavior or the market-scoped catalog.

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 with no filler, and the key constraint (slug lookup) appears early. The phrasing is slightly convoluted ('All the data of one of the lawn mowers in...'), costing it a point but no real waste.

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 does carry the burden of describing returns and does so at a high level. For a two-parameter, market-scoped lookup it should still state what happens for an invalid slug and how market affects the result.

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 enum is well documented in the schema, but slug has no schema description, and the description only supplies 'by its slug' as meaning. It never mentions market, so it misses the chance to explain that the same slug may resolve differently per catalog.

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 resource ('one of the lawn mowers in YardRider's comparison') and the lookup key ('by its slug'), so an agent knows this is a single-record fetch rather than a search. It does not explicitly contrast itself with siblings like search_models or compare_models, which keeps it below 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 its slug' signals the tool is for when a slug is already known, but there is no statement of when to prefer it over search_models or fetch, and no exclusions or prerequisites. An agent must infer the routing decision from the sibling names alone.

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, and destructiveHint=false, so the safety profile is covered. The description adds genuine value beyond them by describing the shape and meaning of the returned data — key, label, possible values or range, unit, and semantics — which matters because there is no output schema. It does not state whether results vary by market or how large the list is.

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 compact sentence with no filler, front-loading the resource and the returned fields. It is a sentence fragment rather than a complete statement of action, which slightly weakens the opening.

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-parameter, read-only, annotated enumerator with no output schema, the description supplies the one thing the structured fields cannot: what each filter entry contains. The only omission is that market-dependent behavior and list size are left entirely to the schema.

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 single 'market' parameter already has a thorough enum description explaining us/fr catalogs and languages. The description adds nothing about the parameter, so the baseline of 3 applies, with the schema doing all the work.

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 identifies the resource precisely — YardRider's lawn-mower comparison filters — and enumerates the fields returned (key, label, possible values or range and unit, meaning). It is a noun phrase rather than a verb+resource statement, but an agent can still tell this apart from siblings like search_models or compare_models.

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 explicit guidance on when to call this versus search, search_models, or compare_models, and no stated prerequisites. The only signal is implicit: that this enumerates the vocabulary the other tools presumably filter on. An agent must infer that relationship itself.

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, non-destructive and closed-world, so the safety profile is covered. The description adds nothing beyond that — it does not say whether the list is scoped by market, whether it is exhaustive, or what an "address" field actually is.

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 short sentence with no wasted words, but it is under-specified rather than efficient — the single clause is not front-loaded with an action and spends its length on the vague "address" detail.

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 one-parameter, read-only list tool with rich annotations and no output schema, this is minimally adequate. It is missing what the returned guides look like and how market changes the result, which an agent would want before calling.

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 market enum fully documents the us/fr choice with catalog implications, so the schema carries the parameter semantics. The description mentions no parameters at all, leaving it purely baseline.

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?

"YardRider's buying guides for lawn mowers" identifies the resource clearly enough, but there is no verb and "with their address" is ambiguous (URL? physical address?). It does not distinguish the tool from siblings like search_models or list_filters beyond the word "guides".

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 guidance on when to use this versus fetch, search, or search_models, and no mention of when a guide listing is preferable to a model listing. The required market parameter and its default go unmentioned.

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?

The annotations already declare this a read-only, idempotent, closed-world operation, so the safety profile is covered. The description usefully adds the return shape (id, title, address) in the absence of an output schema, but says nothing about result limits, ranking, or behavior on empty results.

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 carries purpose, scope, and return values with no filler. It is slightly run-on with stacked parentheticals about Amazon.com/Amazon.fr, 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 two-parameter read-only search with no output schema, the description covers purpose, market scoping, free-text semantics, and returned fields. The remaining gap is differentiation from the similarly named 'search_models' sibling rather than missing core information.

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, while 'query' has no schema description. The description compensates partly by labeling the query 'free text', but adds no syntax, matching behavior, or example queries.

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 lawn mowers in YardRider's comparison'), scopes it to two markets, and states the returned fields (id, title, address). It does not, however, distinguish itself from the sibling 'search_models', which an agent could easily confuse with this tool.

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?

It implies free-text usage and the market choice, and the enum descriptions clarify which locale each value maps to. There is no explicit when-to-use guidance relative to 'search_models', 'compare_models', or 'get_model', leaving alternatives 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 readOnly, idempotent, and non-destructive behavior, so the description only needs added context — and it supplies it, disclosing the returned fields (name, brand, filter values, official data with source, model page link) and noting that today's price and ratings live on the linked page rather than in the result. It does not mention pagination or how limit bounds results beyond the schema 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?

A single front-loaded sentence covering corpus, markets, input modes, and return contents without filler. The semicolon-separated return clause is dense but every 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?

With no output schema, the description properly covers return values and explains where price and ratings live, which is essential for a search tool. The nested filters object is only loosely explained ('keys and values of the comparison's filters'), leaving the agent dependent on list_filters, which is a minor gap rather than a blocking one.

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% and the market enum and query semantics are already documented in the schema, so the description mostly restates them. The only added nuance is that filters are 'exact', which the schema also states, so it sits at the baseline for a well-schematized tool.

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 and resource ('search the lawn mowers in YardRider's comparison') plus the two supported markets, so the agent knows exactly what corpus is being queried. It never names a sibling however, so the boundary against get_model, compare_models, or plain search must be inferred.

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: 'by need (free text) and exact filters' tells the agent when this tool fits, but there is no explicit when-not guidance and no pointer to compare_models or get_model as follow-up alternatives.

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