WashEdge (US)

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 without help from the description. The description adds the 2-4 range constraint, but says nothing about what the comparison output contains or whether all models must share criteria. With annotations carrying the behavioral load, a 3 is appropriate.

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, front-loaded with the verb and the 2-4 constraint, with no filler. The phrase 'from WashEdge's comparison (slugs)' is slightly tangled and could state the slug source more cleanly.

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 explain what a side-by-side comparison returns, and it does not say whether the result is a criteria table, best-pick summary, or per-attribute matrix. It also omits how market interacts with slug validity, which matters because the two markets have separate catalogs.

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-schema, while slugs has no description at all. The description only restates that slugs are the input and that 2 to 4 are allowed, which duplicates minItems/maxItems already in the schema, and it never mentions market or the fact that market selects an entirely separate 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?

States a specific verb (compare) and resource (washing machines) plus a scope constraint of 2 to 4 items, so the action is unambiguous. It does not distinguish itself from siblings like get_model or search_models, leaving the agent to infer why a dedicated compare tool exists.

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 gives no explicit when-to-use guidance, no statement about where valid slugs come from, and no comparison against fetching each model individually. The parenthetical '(slugs)' hints at the identifier source but never tells the agent to obtain slugs from search_models 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 readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful return-content context beyond annotations by listing the categories of data returned, though it does not discuss error behavior or market-specific effects.

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 little waste. The phrasing 'our data' and 'official data' is slightly vague and repetitive, but overall it is efficient.

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 should explain return values more fully; it lists content categories but not structure. It also omits differentiation from the sibling get_model, leaving a meaningful gap for tool selection.

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, while id has no schema description. The description compensates partially by saying the id comes from search, but it does not mention the market parameter or its effect.

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 resource and return content: 'full content of one of WashEdge's washing machines' including filter values, official data, and address. However, it does not distinguish this tool from the sibling get_model, and the verb 'fetch' is generic.

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 usage by noting the id is 'returned by search,' which helps an agent know this is a follow-up to search. But it gives no explicit when-not guidance and does not explain when to use this instead of get_model or search_models.

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 safety is covered. The description adds useful return context ('all the data' plus the page link), but says nothing about behavior for an unknown slug or a market mismatch, which is the main open question for a lookup tool.

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, and the key identifier is stated early. The phrasing 'All the data of one of the washing machines' is slightly convoluted but costs little.

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 and no output schema, the description covers the core retrieval and return shape. It falls short on how to obtain a valid slug, the market's effect on results, and failure behavior, 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 coverage is 50%: the market parameter is fully documented in the schema, while slug has no description at all. The description only echoes that the slug identifies the model; it adds no format, source, or case-sensitivity detail to compensate for the undocumented parameter.

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 washing machine model in WashEdge's comparison) and a specific lookup key (slug), plus what comes back (all data and the page link). It is clear enough to distinguish from search_models and compare_models, though it never explicitly names those siblings.

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 when-to-use or when-not-to-use guidance. The phrase 'by its slug' only implicitly signals that a prior search is needed to obtain the slug, and nothing tells the agent when to prefer compare_models or search_models instead.

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 establish that this is a read-only, idempotent, closed-world, non-destructive call, so the safety profile is covered. The description adds the shape of each returned filter entry, which is real value. It does not mention that results are scoped to a single market's catalog, nor whether the list is stable or paginated.

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, front-loaded with the resource and then the returned fields. Nothing is redundant. The only weakness is that it reads as an elliptical fragment rather than a statement of what the tool does.

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 trivial read-only lookup with one fully documented enum parameter and no output schema, the description carries the necessary burden by enumerating the returned fields. Missing is any statement that the list is market-specific and any hint about its size or ordering, but those are minor for this complexity level.

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 'market' parameter has full schema coverage, including both enum values, their languages/domains, and the note that each market has its own catalog. The description adds nothing about the parameter. With coverage at 100%, the schema 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 names the exact resource (WashEdge washing-machine comparison filters) and enumerates the returned per-filter fields: key, label, possible values/range, unit, and meaning. That is specific enough to distinguish it from compare_models, get_model, and search_models. It loses a point because it is a noun phrase with no verb, leaving 'list' to be inferred from the name/title.

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 guidance, no prerequisites, and no mention of any sibling. The most likely use — calling this before search_models or compare_models to discover which filter keys are valid — is only implied. An agent must infer its place in the workflow.

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 elsewhere. The description contributes one behavioral hint about the payload (each guide comes with an "address"), but the term is too vague to tell the agent whether that is a URL, a citation, or a physical location. No pagination, result-count, or ordering behavior is mentioned.

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 sentence with no filler, and the resource is named immediately. The brevity crosses into under-specification rather than waste, but as a structure judgment it is efficient and front-loaded.

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 is the only place an agent can learn what a "guide" record contains, and it offers just the opaque word "address". For a market-scoped listing tool with no output schema, this leaves the agent guessing at the shape and volume of results.

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 enum for market is documented in detail (us = Amazon.com in English, fr = Amazon.fr in French, own catalogs). The description adds nothing beyond that, so the baseline 3 applies; it notably does not restate that each market returns a separate 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?

"List buying guides for washing machines" is a recognizable verb+resource, so the core purpose is inferable. However, the trailing phrase "with their address" is ambiguous (URL? physical address? brand address?) and the description never distinguishes this from siblings such as fetch or search, which could plausibly also surface 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?

No when-to-use guidance at all: nothing says when to call list_guides versus fetch or search, whether guides are per-market only, or what the agent should do with the returned addresses. The only scoping signal (market) is carried entirely by the schema, not the description.

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 establish read-only, idempotent, non-destructive, closed-world behavior, so the description only needs to add context beyond that. It does add useful detail: results include id, title, and address, and each market is a separate catalog with its own language. It does not cover result limits, ranking, or empty-result behavior, keeping it below a 5.

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 the verb and resource up front and no filler. The nested parenthetical market definitions make it denser than necessary, and the return-field list could stand alone more cleanly.

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 search with no output schema, the description usefully sketches return fields and market scoping. But given five sibling tools including a second search tool, the missing disambiguation leaves the agent without enough to choose confidently.

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 with enum and default, and the description largely restates that mapping. It does add meaning for the undocumented query parameter by labeling it free text, but gives no guidance on syntax, matching behavior, or how many results are returned.

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 (search) and a specific resource (washing machines in WashEdge's comparison catalog), plus the two supported markets. It falls short of a 5 because it never distinguishes itself from the sibling tool search_models, leaving the agent unsure which search 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 '(free text)' note implies the query is a loose keyword lookup, and the market mapping explains the two catalogs. However, there is no explicit statement of when to use this versus search_models, compare_models, or fetch, and no exclusions or prerequisites 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 establish read-only, idempotent, closed-world behavior, so the description's contribution is the return content: name, brand, filter values, official data with its source, and a link. The note that today's price and ratings live on the linked page rather than in the response is a genuinely useful disclosure that shapes how an agent answers follow-ups.

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 opens with what is searched before piling on market and return-value details. The stacked parentheticals make it dense to parse, but no sentence 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 no output schema, the description correctly carries the burden of describing return fields and even the off-response price/rating location. The remaining gap is operational: for a tool with a free-form nested filters object, the missing pointer to 'list_filters' is the one thing an agent is likely to trip over.

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 the baseline is 3; the description mostly echoes what the schema already says about 'market' (us = Amazon.com/English, fr = Amazon.fr/French). It adds only slight meaning by clarifying that filters are 'exact', and says nothing about 'limit' or the min/max range form of the nested filters object.

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 resource (washing machines in WashEdge's comparison) plus the two supported markets, which is far more than a restatement of the name. It does not, however, distinguish itself from the generic 'search' or 'fetch' siblings, so an agent still has to guess which entry point to use.

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 through 'by need (free text) and exact filters', which tells the agent what inputs drive a query, but there is no explicit when-to-use versus 'get_model', 'compare_models' or the plain 'search' tool. Crucially, it never says to call 'list_filters' first to discover valid filter keys and values, which the free-form filters object all but requires.

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