iDevice Wearables

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

Annotations already mark readOnlyHint=true and openWorldHint=false, and the description adds meaningful behavior: each cell carries Confirmed/Rumored/Our read with source outlet and date, and cross-category comparisons are limited to shared structured facts. This adds context beyond the annotations without contradicting them.

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?

Four sentences, each earning its place: purpose, provenance, cross-category behavior, and filter semantics. It is front-loaded with the primary action and contains no filler or redundant restatement.

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 2-parameter, read-only comparison tool with no output schema, the description covers what the response cells contain, how cross-category comparisons behave, and how `specs` filters results. It does not spell out the full response format, but annotations and schema handle read-only intent and cardinality, leaving only minor 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds real value by explaining that `specs` filters rows by keyword with an example (['battery','sleep']) and that `product_slugs` can span different categories with shared-facts-only comparison. This goes beyond the schema's minimal field descriptions.

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: 'Side-by-side specs for 2 or 3 products' and ties it to a known engine (idevice.com/compare). It is clearly distinct from single-product or claim-searching siblings like get_product and search_claims.

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 intended use is implied clearly: compare exactly 2 or 3 products. However, it does not explicitly name alternatives or state when not to use it, such as 'for a single product use get_product instead'. The cross-category caveat helps but does not provide full routing guidance.

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 and openWorldHint=false, so the description does not need to restate safety. It adds useful context about the output structure (statuses, counts, first reporter, link), but does not mention any edge cases, pagination, or potential for large responses. Given the annotation coverage, the description adds moderate value without contradictions.

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, information-dense sentence that front-loads the core purpose and then lists the key output attributes. It is concise and well-structured, though slightly long, but every phrase adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with two parameters and no output schema, the description sufficiently explains what the tool returns, including statuses, counts, first reporter, and evidence link. It does not cover error cases or empty results, but for the likely simple use case, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Both parameters are fully described in the schema (100% coverage), so the description does not need to explain them further. The schema already covers the purpose of category and product_slug, and the description adds no additional parameter context. The baseline score of 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 clearly states the tool's function: it retrieves every well-sourced claim for a single product, and enumerates the specific data returned (statuses, outlet count, first reporter, evidence link). This specificity distinguishes it from the sibling search_claims, which implies cross-product search capability.

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 provides no explicit guidance on when to use this tool versus alternatives like search_claims or compare_products. It implies a need for a product_slug, but does not state when a search across products would be more appropriate or when to prefer this over other 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, so the main safety behavior is covered. The description adds useful context by noting that price may be official or estimated and that the tool handles products with zero claims, but it does not describe response shape, error behavior, or the meaning of 'well-sourced' beyond the phrase itself.

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 carry all the information: the first lists the returned fields in order of importance, and the second adds the no-claims edge case. There is no filler or redundancy.

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 lookup with no output schema, the field list gives an agent a clear picture of what will be returned, and the no-claims note covers the main edge case. It is slightly incomplete only in not clarifying how this relates to get_product_facts/get_claims, but that is more a usage-guideline concern.

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 input schema has 100% coverage for the single parameter product_slug, including a source ('From list_products') and an example ('apple-watch'). The description adds no parameter-specific detail, so it correctly relies on the schema.

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 title 'Get one product's status' identifies the verb and resource, and the description enumerates the returned data (lifecycle, price, key dates, claim count and sourcing quality). It does not explicitly distinguish itself from get_product_facts or get_claims, 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?

The phrase 'Works for products with no claims yet' implies the tool is safe to call when a product has no claims, which is a useful edge-case guideline. However, the description never states when to prefer this tool over get_product_facts, get_claims, or compare_products, leaving the choice to inference.

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

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

Annotations already declare readOnlyHint=true, so the description doesn't need to restate safety. It adds valuable behavioral context: price may be an estimate if not officially announced, and every spec is marked with a confidence level and source. This goes beyond the schema and 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?

Two sentences, front-loaded with the most important content (price, dates, specs) and then the sourcing nuance. Every sentence earns its place; no filler or repetition of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only single-parameter tool with no output schema, the description covers the key return values and their trust levels. It doesn't describe the exact response structure, but with no output schema and a simple parameter, the description is sufficient for an agent to select and invoke the tool correctly.

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 only parameter, product_slug, is already described with an example. The description adds context by implying the slug identifies the product whose facts are returned, but it doesn't need to add more because the schema fully documents the 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 states a specific verb ('Get') and resource ('product facts'), and enumerates the exact content: price, dates, and specs. It also distinguishes itself from siblings by focusing on facts with source labels, which is not covered by get_product or list_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 description implies when to use this tool: when you need price, dates, or specs with sourcing. It does not explicitly name alternatives or exclusions, but the sibling list and the tool's specific scope make the usage context clear. A small gap is not stating when to prefer get_product or compare_products.

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 and openWorldHint=false, so the read-only nature is covered. The description adds useful behavioral context: it returns lifecycle stages, claim counts that can be 0, and optional name filtering. It does not mention pagination, ordering, or response details, but it goes beyond the annotations meaningfully.

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 sentences carry all essential information: what is returned, the lifecycle/claims detail, the primary use-case, and the optional filter. No fluff or repetition; the most important guidance 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, the description still communicates the key return elements: products, lifecycle, claims count, and product_slug. It stops short of specifying response shape, pagination, or ordering, which is a minor gap for a list tool that could return many products.

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 only parameter, query, is already fully described in the schema as 'Words in the product name'. The description reinforces that it is optional and gives another example, but adds little meaning beyond the schema. With 100% schema description coverage, 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?

Description states a specific verb-resource pair ('list products'), defines scope ('Every product iDevice tracks'), and adds distinguishing attributes (lifecycle, claims count). It also names its role in the workflow ('Use this first to find the product_slug'), which differentiates it from siblings like get_product and search_claims.

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 explicitly says to use this tool first to find the product_slug for other tools, giving clear contextual guidance. It does not explicitly name when-not-to-use or alternative tools, but the primary use case is well communicated.

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 mark the tool read-only, and the description adds behavioral context by stating results are filtered to 'well-sourced' claims and include status and product lifecycle. It does not contradict the annotations and gives useful detail beyond the structured hints, though it omits ordering or pagination behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences carry the full message; the purpose is front-loaded and examples are packed into the first sentence. The final sentence about result fields adds meaningful context without 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 usefully discloses the high-level result shape ('status and product lifecycle') and the cross-product scope. It is adequate for a simple two-parameter search tool, though a bit more detail about ordering or result contents would further remove ambiguity. Sibling differentiation remains implied rather than explicit.

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 input schema fully documents both parameters (query with minLength and limit with min/max/default), so the description does not need to repeat them. It adds only usage examples for query values ('battery', 'display', 'price'), which is helpful but not essential. Baseline 3 applies because schema coverage is 100%.

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 uses a specific verb ('Find'), a clear resource ('well-sourced claims'), and an explicit scope ('across every tracked product'). It also gives concrete query examples, clearly distinguishing this cross-product keyword search from siblings like get_claims or list_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 description implies the tool is for keyword-oriented claim discovery across all tracked products. However, it does not explicitly state when to prefer search_claims over get_claims or list_products, nor does it give any 'when not to use' conditions.

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