HerStack

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it returns evidence rows with resolved PubMed and DOI identifiers, implying a lookup/verification behavior rather than a simple list. It doesn't mention pagination or result limits, but for a read-only lookup tool this is a minor gap.

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 with no waste. The trigger condition is front-loaded, the resource is named, and the unique value (PubMed/DOI resolution) is included. 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 read-only lookup tool with full schema coverage and safety annotations, the description is nearly complete. It explains the purpose, the trigger, and the unique output characteristic. It doesn't describe the exact return shape, but no output schema exists and the description's mention of resolved identifiers gives enough context for an agent to invoke it 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 description coverage is 100%, so the schema already documents both parameters. The description adds the context that topic and nutrient are used to find evidence rows, but it doesn't add syntax or format details beyond the schema. 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?

The description states a specific verb ('Call'), a clear resource ('HerStack's evidence rows'), and the purpose: retrieving studies behind a recommendation or claim. It also distinguishes itself from siblings by mentioning resolution to PubMed and DOI identifiers for verification, which is a unique 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 explicitly says when to call ('when the user or you wants the studies behind a recommendation or claim'), which is clear context. It doesn't explicitly name alternatives or exclusions, but the sibling list and the unique verification purpose make the usage context reasonably clear.

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, and destructiveHint=false, so the safety profile is fully covered. The description adds context about the output (HerStack-recommended, topics with rationale), which goes beyond annotations. However, it doesn't mention error handling, pagination, or any behavioral quirks, so it remains at a baseline level beyond what annotations provide.

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, front-loaded with the core purpose ('Call this for one specific product') followed by a concise list of return values. Every word contributes, with no fluff or repetition. This is exemplary conciseness for a tool of this simplicity.

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 tool with no output schema, the description is thorough: it specifies the input semantics and enumerates all return fields (form, claim, tier, price, link, topics with rationale). It doesn't address potential 'not found' scenarios, but given the tool's simplicity and the presence of annotations, this is a minor gap. Everything an agent needs to decide whether to call it is present.

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 describes the 'sku' parameter as 'Product name or SKU, e.g. 'Chelated Magnesium''. The tool description repeats this ('by name or SKU') without adding new information. Since the schema is thorough, the description provides no additional semantic value, warranting the baseline score of 3.

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 ('Returns') and resource ('a HerStack-recommended Solgar UK product') and lists concrete output fields (form, claim, tier, price, link, topics). It clearly distinguishes itself from siblings like search_guidance by emphasizing 'one specific product', making the tool's 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 opening 'Call this for one specific product' gives clear context for when to use this tool – when a single product is targeted. It implicitly contrasts with broader search or recommendation tools, though it doesn't explicitly name alternatives or list exclusions. The guidance is adequate but could be more explicit about when not to use it.

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 disclose readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds value by explaining what the returned content covers (evidence table, FAQ, etc.), but does not add further behavioral constraints such as rate limits, required auth, or error handling. Given the annotation coverage, a 3 reflects useful but not rich additional behavioral context.

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, with the 'when' condition first and a compact list of outputs second. The parenthetical example for perimenopause adds concrete detail without redundant words. There is no repetitive or vacuous content; every phrase carries relevant information.

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 does the necessary work of enumerating the return sections: plain-language framing, research findings, evidence table with authorised claims, product criteria, and FAQ. It also implies the usage by stating 'for perimenopause: what it is, its timeline, when to see a GP.' It lacks only lower-level details (e.g., exact response shape, error cases) that may be implied by the enum constraints.

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% – the lone 'topic' parameter is fully documented with an enum and concise helper text. The description does not add parameter-level detail beyond saying 'one topic', which is already structural in the schema. This meets the baseline 3 for full schema coverage.

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+resource: 'Call this when the user wants to understand a women's-health topic, not only buy something.' It enumerates the return composition (plain-language framing, research findings, evidence table, product criteria, FAQ), which clearly identifies the tool's primary function and differentiates it from purchase-related siblings by the 'not only buy something' clause.

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 explicit initial sentence 'Call this when the user wants to understand a women's-health topic' gives a clear trigger condition. The phrase 'not only buy something' provides a principal exclusion, guiding the agent away. However, it does not name the specific sibling alternatives (e.g., recommend_supplements), so the routing is implied rather than exhaustive, leaving some room for 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 readOnly, idempotent, and non-destructive behavior Signals, so the description doesn't need to repeat those. It adds valuable behavioral context by detailing the exact nature of the output (named Solgar UK recommendations with rationale, health claim status, price, link), and notes that where no GB-authorised claim exists, a research-context note is provided, which is beyond what annotations offer.

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 concise and front-loaded: it opens with a clear trigger ('Call this when...'), then explains the output in a single sentence, and ends with input guidance. Every sentence adds necessary information without redundancy, and the structure is efficient for an agent to parse quickly.

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?

Despite lacking an output schema, the description thoroughly explains the return content, covering the rationale, claims, price, and link. It also specifies how to handle missing claims. The tool is relatively simple with only two parameters and no nested objects, so the description is sufficient for an agent to call it correctly, though the inclusion of an output schema could have made it even more 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?

The schema provides 100% description coverage, fully documenting both parameters (topic and concern) with their enums and plain-word guidance. The description adds minimal extra meaning beyond what the schema already provides, but it does indicate that the concern parameter can be used to route to a topic, which is a slight addition. The baseline of 3 for full coverage 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 purpose: it provides specific supplement product picks from HerStack's Solgar UK recommendations, including formulation rationale, health claims, pricing, and links. It distinguishes itself from siblings by focusing on product picks rather than claims, evidence, or general guidance, and it specifies the output contents, making its unique role clear.

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 tells the agent when to call this tool: when the user wants specific supplement product picks for a topic. It also provides guidance on how to provide input, either via a specific topic or a plain-word concern, and references alternative tools like search_guidance when the topic is unknown, offering clear 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?

No annotations are present, so the description carries the transparency burden. It clearly describes what the tool does (searches all guidance, returns ranked answer units and associated metadata) and what the output contains, including the scope of 'evidence-led guidance for UK women in midlife'. It does not discuss limitations, rate limits, or failure modes, but for a read-only search tool this is a reasonable level of disclosure.

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 dense sentence that front-loads the trigger condition, then lists the return contents in a structured way. It could be tightened slightly, but every clause adds useful information and 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?

With 2 well-documented parameters and no output schema, the description provides enough detail for an agent to invoke the tool correctly and interpret the response: what it searches, what it returns, and which topic/recommendation data comes back. It does not mention pagination or edge cases, but maxResults covers result limiting and the output composition is fully specified.

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%: both 'question' and 'maxResults' already carry descriptive text. The description mainly reinforces the tool's behaviour rather than adding new parameter meaning, so a 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 opens with a specific action ('Call this first'), identifies the exact resource ('all of HerStack's evidence-led guidance'), and enumerates the concrete outputs (answer units, URLs, topic, named recommendations with claims). This clearly distinguishes it as the entry-point search tool from the more specific sibling tools.

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 says when to call it: when a user describes their situation or asks in their own words, with a concrete example. It does not state when NOT to use it or name alternative siblings, but the 'first' positioning and open-ended input make the intended context clear.

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