YardRider (France)

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 by structured data. The description adds the useful detail that models are compared "côté à côte sur les mêmes critères", but says nothing about what criteria are used, how many results come back, or how the market parameter changes the 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 compact sentence with the key constraint (2 à 4) front-loaded and zero redundant wording. It is efficient, though its terseness leaves the gaps noted in other dimensions.

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 tool with no output schema, the definition is minimally adequate: it tells the agent the input shape and purpose. It omits what the comparison output contains and the fact that the market value selects a whole separate catalog with its own language, which matters for correct invocation.

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 thoroughly documented in the schema, while slugs has no schema description at all. The description partly compensates by naming slugs and restating the 2-4 bound, but adds no format or sourcing detail for the slugs beyond what minItems/maxItems already encode.

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 and resource: "Compare 2 à 4 tondeuses du comparateur YardRider (slugs)", so an agent immediately knows it produces a side-by-side comparison of 2-4 mowers identified by slug. It is clear but does not explicitly differentiate itself from siblings such as get_model or search_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?

Usage is only implied: the mention of the comparator and the 2-4 count suggests when this tool is appropriate, but there is no explicit "use this instead of get_model when comparing multiple products" guidance. No exclusions, prerequisites, or alternative routing are given.

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 safety is covered. The description adds genuinely new behavioral context by enumerating the returned payload components (own data, filters, official data, address), which matters because no output schema exists. It stops short of noting behavior for an unknown id or how the two markets' catalogs differ in practice.

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 id provenance is given inline. The trailing list is slightly cryptic ('nos données', 'son adresse' read as internal shorthand for own dataset and URL), costing a little clarity for the density gained.

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 with annotations covering safety and no output schema, the description does the important work of listing return contents. Gaps remain: the overlap with get_model is unresolved, and behavior for an invalid id or a model absent from the chosen market is unstated, which an agent selecting among six siblings would benefit from knowing.

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-schema with its enum and default, while the required id has no schema description at all. The description compensates for exactly that gap by stating the id comes from search, which is the key semantic an agent needs. Nothing is added about market, but the schema already handles it fully.

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 retrieval action on a concrete resource: the full content of a model from the YardRider lawnmower comparator, and it enumerates what that content includes (our data, its filters, official data, its address). It names 'search' as the source of the id. However, it does not distinguish itself from the sibling get_model, which plausibly retrieves the same resource, leaving the agent to guess between them.

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 '(id renvoyé par search)', which tells the agent this is a follow-up call after a search and where the id comes from. There is no explicit statement of when to prefer this over get_model or compare_models, and no exclusion or precondition beyond the id.

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 useful content about the return payload (all model data plus a link to its detail page), but says nothing about behavior when a slug does not exist in the given market.

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 compact sentence with the key scoping detail (the slug) front-loaded and no filler. It could be tightened only by adding genuinely useful information rather than by cutting anything.

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 complete annotations and no output schema, the description adequately covers what data comes back and how the record is addressed. It is still thin on slug provenance/format and on market-specific behavior, which matters since the schema notes each market has its own catalog.

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 richly documented in the schema with enum semantics, while slug is a bare string. The phrase 'par son slug' partially compensates by clarifying that slug is the model's identifier rather than an arbitrary string, but it gives no format, source, or example.

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 ('récupérer toutes les données'), a specific resource (a model of the YardRider lawnmower comparator), and the lookup key (slug), plus mentions the returned 'lien vers sa fiche'. It does not, however, differentiate itself from siblings like search_models or compare_models that plausibly return similar model data.

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: '(par son slug)' signals that the caller must already possess a slug, which indirectly points to search_models as the discovery step. There is no explicit when-to-use statement, no exclusion of siblings, and no mention of what to do if 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 fully covered. With no output schema present, the description earns credit for disclosing the shape of the response (key, label, possible values or range with unit, explanation), which is exactly the missing behavioral detail. It does not mention ordering, count, or whether the set varies by market beyond what the schema states.

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 dense sentence that front-loads the resource and then enumerates the returned fields, with no filler or redundancy. Every element earns its place and nothing could be removed without losing 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 one-parameter read-only listing tool with rich annotations and a fully documented schema, the description supplies the one thing structured fields cannot: the response contents. The main remaining gap is the absence of guidance on when to call it relative to compare_models or search_models, but nothing needed to invoke it correctly 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 single 'market' parameter has an enum with a detailed description of what fr and us mean, so the schema does the heavy lifting. The description adds no additional meaning about the market parameter, which matches the baseline of 3 for high-coverage schemas.

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 the resource precisely ('les filtres du comparateur de tondeuses YardRider') and enumerates the content returned: clé, libellé, valeurs possibles ou plage et unité, explication. It lacks an explicit verb and does not distinguish itself from siblings like list_guides or compare_models, but an agent can still tell this returns filter metadata rather than models or 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?

Usage is only implied: an agent would infer this is the discovery step for available filter keys and values before calling compare_models or search_models. There is no explicit when-to-use statement, no exclusion of alternatives, and no mention of ordering relative to sibling tools.

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 only that the guides come with their address/URL, a small piece of output context that is genuinely useful but thin.

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 short fragment with no filler, and the resource is stated up front. It is efficient but reads as a label rather than a self-contained statement of what the call does, which slightly limits clarity.

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?

This is a simple, single-parameter, read-only tool with full schema documentation and no output schema, so not much is required. Still, the description could state that it enumerates all guides for the selected market and that each entry carries a URL, which would fully close the 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 market enum is documented in detail (Amazon.fr vs Amazon.com, separate catalogues per market). The description says nothing about the market parameter or how it changes the returned guides, so it adds no meaning beyond the schema; the 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 identifies the resource (YardRider buying guides on lawnmowers) and adds a return-field hint ('avec leur adresse'), which goes beyond the bare name/title. However, it is a noun phrase with no verb describing the operation and gives no differentiation from siblings like search, fetch, or compare_models, so an agent must infer that this is a bulk listing call.

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 tool versus the six sibling tools (search, fetch, list_filters, compare_models, search_models, get_model). It also does not say whether this is a full listing covering all markets or whether a specific market must be chosen 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?

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds the returned fields (id, titre, adresse) but says nothing about result limits, ranking, or whether results are cached – modest added value.

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 dense but well-ordered sentence: action, resource, markets, input format, and return shape. Nothing is wasted, though the parenthetical nesting of site/language pairs takes a second read.

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 no output schema, the description covers input nature, market selection, and return fields. Missing only result-set size or ordering behavior, which is minor here.

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 schema documents 'market' richly but leaves 'query' undocumented; the description compensates by stating the query is free text ('texte libre'). It also ties each market code to a site and language, reinforcing the enum 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?

Names a specific verb and resource ('Cherche des tondeuses dans le comparateur YardRider') and scopes it to two named marketplaces. It stops short of explicitly distinguishing itself from the sibling search_models, which also sounds like a search 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?

Conveys that queries are free text and that each market has its own catalogue, which implicitly tells the agent to pick a market. It gives no explicit when-to-use or when-not-to-use guidance relative to search_models, get_model, or fetch.

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, non-destructive and closed-world, so the safety burden is light. The description still adds real value by enumerating the returned fields (nom, marque, valeurs des filtres, données officielles avec leur source, lien) and by clarifying that today's prices and ratings live on the linked product page rather than in the response. That distinction between in-result data and on-site data is not derivable from the annotations.

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: verb and resource first, then markets, then search modes, then return contents. No filler or redundant framing. It is dense with parentheticals, 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 four parameters, a nested 'filters' object, and no output schema, the description correctly spends its words on describing the return payload and the per-market catalogue split. The main remaining gap is that it never says how to discover valid filter keys/values (the list_filters sibling) and does not touch 'limit' behavior, which matters for an open filter object.

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 schema already documents 'query' and 'market', including the fr/us locale mapping, which the description largely repeats. The description does add meaning for 'filters' by framing them as exact values paired with a free-text need, but says nothing about 'limit' and gives no examples of valid filter keys or range syntax beyond what the schema shows. Baseline 3 is appropriate.

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 opens with a specific verb and resource ('Cherche des tondeuses dans le comparateur YardRider') and scopes it to two marketplaces, so the agent knows exactly what domain is searched. It also states the two search modes (free-text need vs. exact filters) and what comes back. It never differentiates itself from the sibling 'search' or 'compare_models', so the sibling disambiguation that a 5 requires is absent.

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: search by a free-text need plus exact filters when looking for mowers in a given market. There is no explicit 'use this instead of X' routing (e.g. compare_models for comparisons, get_model for one item, list_filters to discover filter keys), even though the description says filters must use 'les clés et valeurs des filtres du comparateur' without saying where to obtain them.

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