MewToys Catalog and Buyer Knowledge

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

No annotations, so the description carries the full behavioral burden. It usefully discloses a non-obvious return trait — output is filtered to differing specification fields only, not the full records — but says nothing about error behavior for out-of-range SKU counts, missing SKUs, or the shape of the comparison result.

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 zero filler, front-loading the operation before the output-filtering detail. Every clause earns its place.

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 comparison tool with no output schema, the description covers the essential operation and the key return behavior (differences only). It stops short of specifying the result layout or edge-case handling, but nothing critical to selecting or calling the tool 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?

Only one parameter, and schema description coverage is 100% with minItems/maxItems already encoding the 'two to four' rule. The description's 'two to four SKUs' merely restates the schema constraint, adding no semantics beyond it, 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?

States a specific verb (compare), resource (SKUs/products), and scope (two to four side by side). The comparative framing plus the 'only fields where they differ' clause makes it unmistakably distinct from sibling read tools like get_product and search_products.

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 two-to-four SKU range and 'side by side' framing imply a comparison task, but there is no explicit when-to-use versus when-not, and no sibling (e.g. get_product for a single SKU) is named. Usage is inferable rather than guided.

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 are provided, so the description must carry the full behavioral burden. It describes the topics returned but does not disclose whether the operation is read-only, whether authentication is required, whether results are cached, or any other operational trait. The name 'get_' implies a read but the description adds no behavioral context beyond content scope.

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 efficient sentence that front-loads the company identity and then lists the remaining content topics. It is appropriately sized for a simple no-parameter getter, with no filler, though the list is slightly run-on.

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 no-parameter tool with no output schema, the description gives a reasonable overview of the content returned (company identity, production location, factory activity, published pages). However, it does not clarify the return format, whether the response is a summary or full pages, or any access constraints, leaving some gaps.

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 tool takes zero parameters, so there are no parameter semantics to explain. Per the rubric, a zero-parameter tool receives a baseline of 4 on this dimension.

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 the resource (MewToys company profile) and enumerates the content it covers: who the company is, production location, factory operations, and relevant published pages. However, it uses a noun-phrase list rather than a specific verb and does not differentiate itself from siblings like search_knowledge or get_document.

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 use this tool versus alternatives. The description implies usage by listing the company information it provides, but it names no conditions, exclusions, or sibling tools that might be better for related queries.

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 are provided, so the description carries the full burden, and it does disclose meaningful mode-dependent behavior: a SKU yields scope and guidance for that product, while omitting it yields published options across the range. It stops short of stating that this is a read-only lookup, whether results can be empty, or anything about permissions or rate limits.

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 well-formed sentence that front-loads the primary case and appends the fallback condition. Every clause carries information; there is no filler.

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 zero-required-parameter lookup with no output schema and no annotations, the description covers both invocation paths and what conceptually comes back in each. It could be slightly richer about what 'guidance' and 'published options' actually contain, but nothing needed to call 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 coverage is 100% and the schema already documents the single optional 'sku' parameter, so the baseline is 3. The description earns above baseline by explaining the semantic consequence of omitting the SKU (falls back to range-wide published options), which the schema does not state.

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 (customization scope/options) and clearly splits it into two modes: one SKU vs. range-wide published options. It is distinguishable from siblings like get_product or search_products, though it never explicitly contrasts itself with 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?

The conditional phrasing ('when no SKU is given') implicitly tells the agent which mode applies based on input, which is usable guidance. However, there is no explicit statement of when to reach for this tool versus get_product or compare_products, and no exclusions or prerequisites.

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 are provided, so the description carries the burden. It usefully notes that only 'published' content is returned, implying a scope/visibility constraint, but says nothing about permissions, error behavior for missing or unpublished ids, or response size/pagination. Adequate but thin for an unannotated 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 zero waste, front-loading the action and result before naming the input source. Nothing needs trimming and nothing essential is buried.

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-optional-parameter read tool with no output schema, the description covers purpose, input source, and result shape ('full text'), which is enough to call it correctly. The remaining gap is guidance on the id/path interaction when neither is required.

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 both parameters have their own examples (ids and paths), so the schema already does the heavy lifting. The description adds only the provenance hint that the id comes from search_knowledge; it does not clarify how id and path interact when both are optional.

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 ('Return the full text of one published page or guide') and scopes it to a single document, which distinguishes it from the sibling search_knowledge that produces candidate ids. An agent can tell immediately this is the fetch-one step after a search.

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?

Explicitly tells the agent where the input comes from ('using an id from search_knowledge or its path'), which is a clear usage condition tying it to the retrieval workflow. It stops short of stating exclusions, such as when to prefer path over id or that unpublished docs cannot be fetched.

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 are provided, so the description carries the full burden. It discloses what content is returned, which is genuinely useful behavioral context, but does not state the read-only nature explicitly, any auth/prerequisite requirements, or whether unpublished SKUs are handled differently. The 'published specification' phrasing hints at a visibility constraint but leaves it ambiguous.

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 communicates the operation and the payload contents with no filler. Every phrase earns its place.

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 tool with no output schema, the description usefully enumerates the return contents so the agent knows what to expect. It is nearly complete, with only the visibility/permission behavior left unspecified.

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% with a single documented 'sku' parameter (including a maxLength and example). The description's 'one SKU' adds no format or semantics beyond the schema, so 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?

States a clear verb ('Return') and resource ('one SKU') and enumerates the returned artifacts (specification, customization scope, packaging, sample review points, gallery). This distinguishes it from list-style siblings like search_products, but it never explicitly contrasts itself with compare_products or get_customization_options.

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 'for one SKU' — retrieve a single product's details — but there is no explicit when-to-use guidance and no alternatives named (e.g., use search_products to find a SKU, compare_products for side-by-side). The agent must infer the selection rule 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?

No annotations are provided, so the description carries the full burden. It usefully clarifies that no prices are returned and that the quotation is handled offline, but it omits whether the link is generated server-side, any auth/permission requirements, and whether calling it has side effects or persists anything.

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?

Two short sentences, zero waste, with the return value front-loaded and the pricing caveat immediately after. Every sentence earns its place.

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 link-builder with no output schema, the description explains what comes back (the contact page link) and why prices are absent, which is the key expectation to set. It is nearly complete, missing only operational details such as link validity or reuse behavior.

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%, so all four parameters (skus, intent, market, quantity) are already documented in the schema, including the enum and max/min bounds. The description only references SKUs and intent and adds no syntax or format detail beyond what the schema provides, 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?

States a specific verb (Return) and resource (contact page link) with scope detail: SKUs and inquiry intent are prefilled. It is distinguishable from siblings like get_product or search_products, though it never names an alternative, so it stops short of full sibling differentiation.

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 note that prices are not published and the quotation follows the buyer's request tells the agent this is the path for a buyer seeking a quote. There is no explicit when-to-use, when-not-to-use, or pointer to any sibling tool.

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?

With no annotations, the description must carry the behavioral load. It does disclose meaningful traits: the result set is fixed at four, limited to published categories, and each entry includes SKUs and category pages. It does not state read-only nature explicitly or any auth/pagination considerations, so 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?

A single tight sentence with no filler, and the resource is front-loaded ahead of the scope and return details.

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 zero-parameter list tool with no annotations and no output schema, the description adequately conveys the scope and roughly what comes back. It lacks only explicit read-only characterization and any hint of ordering or pagination, minor for a fixed four-item set.

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 tool takes zero parameters (schema coverage 100%, no properties), so the baseline is 4. There is nothing for the description to clarify beyond confirming the call needs no input.

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?

Clear specific verb plus resource ('List ... categories') with scope detail about which categories ('the four published') and what they carry ('SKUs and category pages'). It does not explicitly contrast itself with siblings like search_products or get_product, so it lands at 4 rather than 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?

There is no statement of when to use this versus search_products or get_product, no prerequisites, and no exclusions. The only implied guidance is that this returns the full fixed taxonomy rather than a filtered search, which an agent must infer.

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 are provided, so the description carries the full behavioral burden. It does disclose the content boundary (product pages excluded), which is genuinely useful scoping, but says nothing about authentication, rate limits, pagination, or result shape for what is a read/search operation.

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?

Two tight sentences: coverage scope first, then the exclusion and the redirect. Every clause earns its place, and the most decision-relevant routing fact is 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?

With no output schema and full parameter documentation in the schema, the description covers the essentials: what corpus is searched and which sibling handles the excluded case. It stops short of describing the return shape or ordering, 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 description coverage is 100%, and the schema already documents query, kind (with its eight values), and limit. The description's content list loosely echoes the kind values but adds no filtering syntax, formatting, or default behavior beyond what the schema states, so 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?

The description states a specific verb (Search) and resource (published material), then enumerates the content domains covered: manufacturing, OEM/ODM, sampling, testing, wholesale, buyer guides, FAQ, glossary, comparisons. It explicitly names the sibling it is not (search_products) for product pages, so an agent can route without opening either schema.

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 gives an explicit exclusion (product pages are excluded) paired with the alternative tool to use instead (search_products). That is a direct when-not/when-instead statement, which is stronger than most search tools provide.

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 are provided, so the description carries the burden. It usefully discloses that only *published* products are returned and enumerates the response fields (SKU, title, category, summary, MOQ, page link), which compensates for the absent output schema, but it says nothing about ranking, auth or rate limits.

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?

Two short sentences, zero filler, with the scope and searchable facets front-loaded before the return-value summary. Every clause earns its place.

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 4-param, all-optional search tool with no annotations and no output schema, the description covers scope-plus-return-shape well. It stops short of clarifying result ordering or how multiple filters combine, which an agent would have to guess.

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 each of the four optional params documented (limit default/max, category enum-ish values, material examples), so the baseline is 3. The description adds no syntax or format detail beyond what the schema already provides.

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+resource ('Search published MewToys cat products') and the three facets it searches over (keyword, category, material). Combined with the title, an agent can distinguish it from siblings like get_product, list_categories and compare_products without opening the schema.

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 implies a discovery/browse use case through the mention of keyword, category and material filters, but it never states when to prefer this over get_product (single lookup) or list_categories, nor any exclusions. Usage is inferable but not spelled out.

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