iDevice Wearables

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds behavioral context by specifying that claims are 'corroborated' and detailing the return fields (outlet count, first outlet, evidence link), which clarifies what the agent can expect. No contradiction with annotations is present.

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, compact sentence that front-loads the core purpose ('Every corroborated claim for one product') and conveys essential return details without filler. Every word 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?

Given the tool's simplicity (2 params, no output schema, read-only annotation), the description adequately explains what the tool returns and its product-specific scope. It lacks edge-case handling details (e.g., no claims), but for a straightforward retrieval tool, this is sufficient.

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% description coverage for both parameters (category filter and product_slug source). The tool description does not add parameter-specific meaning beyond what the schema already provides, so 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 retrieves all corroborated claims for a single product and lists the specific data points (reported info, number of outlets, first outlet, evidence link). This distinguishes it from sibling tools like get_product and search_claims, making its purpose unambiguous.

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 usage for a specific product via 'for one product' and the product_slug parameter, but it does not explicitly mention when to use it instead of search_claims or other alternatives. No exclusions or alternative tool references are provided, leaving usage guidance implicit rather than explicit.

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's main contribution is listing the specific data fields returned (status, category, sourcing). This adds context beyond annotations but does not disclose any additional behavioral traits such as error handling, performance, or requirements beyond the slug. The description does not contradict 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?

The description is a single concise sentence that front-loads the primary return fields. There is no fluff or redundancy. Every phrase adds value, and the structure is ideal for quick comprehension.

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?

Given the tool's simplicity (one parameter, no output schema), the description covers the essential return fields (status, category, sourcing) but lacks details on output formatting, possible values, or error conditions (e.g., product not found). Sibling tool names provide some context, but the absence of an output schema places more burden on the description to define the return structure, which it does only at a high 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 schema has one parameter, product_slug, with zero description coverage. The tool description does not explain what product_slug is, its format, or how to obtain it (e.g., from list_products). The parameter name is somewhat self-explanatory, but with 0% coverage and no elaboration in the description, the agent receives minimal help in constructing correct 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?

The description specifies exactly what the tool returns—release status, category, and claim sourcing—using the resource 'product' and scope 'one product.' The title adds the verb 'get,' making it clear this is a retrieval operation. It distinguishes itself from sibling tools like get_claims and list_products by focusing on a single product's aggregated status.

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: if you need a single product's release status, category, and sourcing quality, use this tool. However, there is no explicit guidance on when to prefer this over list_products or get_claims, nor any discussion of prerequisites (e.g., needing a valid product_slug from another tool). No exclusions or alternative contexts are mentioned.

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 indicate read-only and closed-world behavior. The description adds a key behavioral criterion—only products with at least one well-sourced claim are returned—which goes beyond the annotations. However, it does not disclose return format, ordering, or pagination, but these are less critical for a parameterless list 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?

The description is two sentences, each earning its place: the first defines the returned items, and the second provides usage guidance. It is compact, front-loaded, and contains no unnecessary fluff.

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?

Given no parameters, no output schema, and helpful annotations, the description is sufficient. It explains what is returned (products with well-sourced claims), how to use it (first to get product_slug), and references the role it plays among sibling tools. This is complete for a simple list operation.

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 has zero parameters and the schema is empty with 100% coverage, so there are no parameter semantics to explain. The description mentions product_slug, but that is an output field for other tools, not a parameter of this tool. The baseline for zero parameters is 4.

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 lists products with at least one well-sourced claim, and the title 'List tracked products' reinforces the action. It distinguishes from siblings like get_claims and get_product by focusing on product enumeration rather than claim retrieval or individual product lookup.

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 'Use this first to find the right product_slug for the other tools,' which is direct guidance on when to use this tool and why it is a prerequisite for sibling tools. This makes the usage context obvious and provides clear operational advice.

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 the tool as read-only, and the description adds meaningful behavior: it searches 'corroborated claims' and returns results across all products. This goes beyond the structured data, though it doesn't disclose pagination or result format, which is acceptable given 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?

Two succinct sentences. The first conveys the core purpose, the second gives usage guidance with examples. No fluff 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?

For a search tool with good annotations and a simple schema, the description is sufficiently complete: it specifies scope, input examples, and when to use it. It doesn't explain return format, but that's not critical given the absence of an output schema and the presence of sibling tools for details.

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%, so the schema fully documents both parameters. The description gives example keywords for 'query', adding slight illustrative value, but doesn't provide additional semantics beyond the schema's own 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 clearly states the verb ('Find'), resource ('claims'), and scope ('across every tracked product'), distinguishing it from sibling tools like get_claims (likely product-specific) and list_products. The keyword-search nature is explicit, and the examples illustrate the intended input.

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?

Provides explicit guidance: 'Use when the product is not known in advance.' This contextualizes when to choose this search tool over product-specific tools, effectively excluding cases where the product is already known.

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