neutral-skincare-reference

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

With no annotations, the description reveals listing is alphabetical and neutral, but lacks details on error handling, missing products, or permissions, which would be expected for full transparency.

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 with front-loaded action and essential distinction, no unnecessary words.

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?

While adequate for a simple tool, the lack of an output schema means the agent doesn't know the format of returned products, and edge cases (e.g., product not found) are not addressed.

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 parameter 'query' is described in the schema as 'The product to find alternatives for.' The description adds that the query should be a product and clarifies the return set (same category), adding value beyond 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 description clearly states the tool returns other products in the same category, listed neutrally (alphabetical), distinguishing it from sibling tools like search_products and list_brands.

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 contrasts this tool with personalized recommendations ('personal best-fit and price-tiered alternatives live in the app'), helping agents decide when to use it, though it doesn't directly compare to siblings.

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 full burden. It states the tool lists brands with product counts, but does not mention permissions, rate limits, or the scope of 'covered' (e.g., all brands? only active?). This is adequate for a simple list, but could be more transparent.

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 contains all necessary information without redundancy. Every word contributes to understanding the tool's function.

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 parameterless tool with no output schema and simple behavior, the description fully explains what the tool does and what it returns. Sibling tools are distinct, and no additional context is needed.

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 zero parameters, so baseline 4 applies. The description adds value by specifying that the output includes brand names and product counts, beyond what the empty schema conveys.

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 specifies that the tool lists dermacosmetic brands covered in MHS BLOOM's reference, including product counts. It uses a specific verb ('List') and resource ('brands covered'), and is easily distinguishable from siblings like 'find_alternatives' or 'lookup_product'.

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 does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. Usage is implied by the purpose alone, which is adequate but not proactive.

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 provided; description compensates by stating it is 'descriptive only' and 'not a personal skin verdict', clearly indicating no side effects or judgment. Could explicitly state read-only.

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 concise sentences, front-loaded with key information, no 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 single parameter, no output schema, and no annotations, the description provides comprehensive context on return value and purpose.

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 covers the parameter fully; tool description does not add extra meaning beyond confirming usage. Baseline score correct.

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 clearly states verb 'identify' and resource 'dermacosmetic product', lists specific facts returned, and distinguishes from siblings by specifying 'neutral reference' and 'descriptive only'.

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?

Implies usage for factual lookups vs personal verdicts but does not explicitly mention when to use this tool over sibling tools like 'search_products' or 'find_alternatives'.

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 provided, the description carries full burden. It discloses that results are 'neutral, alphabetical list with reference links' and 'Never ranked or rated', which reveals key behavioral traits. However, it does not mention read-only nature, auth requirements, 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?

The description consists of two concise sentences. The first states the core purpose, and the second adds key behavioral details. No extraneous information is present.

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 complexity (6 parameters, no output schema, no annotations), the description covers purpose, parameters, and output behavior (alphabetical list with reference links). It omits error handling and auth requirements, but for a search tool with sibling tools, it is largely 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?

Schema coverage is only 17%, but the description lists all parameters (ingredient, concern, category, brand, fragrance_free) with examples and clarifies they can be used in any combination. This adds significant meaning beyond the sparse schema, although it lacks detailed format constraints.

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 'Search' and resource 'MHS BLOOM's reference for products' with specific search criteria (ingredient, concern, category, brand, fragrance_free). It differentiates from siblings by noting output is 'neutral, alphabetical list with reference links' and 'Never ranked or rated', distinguishing it from tools like find_alternatives or lookup_product.

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 by listing what can be searched, but it does not explicitly state when to use this tool versus siblings (find_alternatives, list_brands, lookup_product). There is no guidance on prerequisites or exclusions.

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