Hera Fertility

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 useful behavioral context: it returns price, payment location, and an order link, and it implies a nearest-lab ranking. It does not disclose details like whether results are sorted by distance or whether availability varies, but the annotations lower the burden.

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 includes a helpful example query. It is slightly long but every clause earns its place; the example query at the end is useful for an agent.

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 two well-documented parameters and no output schema, the description covers the main inputs, the use cases, and the kind of output (price, payment location, order link). It does not specify the output format or sorting, but the annotations and schema cover the rest. The tool is simple enough that this is nearly 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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds a little context by listing the test types in prose and noting the default, but it does not add meaning beyond the schema's own descriptions. 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 ('find'), a resource ('Hera partner labs near a US ZIP code'), and the exact use cases (semen analysis, post-vasectomy check, etc.). It also includes a concrete example query ('where can I get a sperm test near me'), which makes the tool's purpose unmistakable and distinguishes it from siblings like get_lab.

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 clearly indicates when to use the tool: for location-based lab searches near a ZIP code, and gives a natural-language trigger. It does not explicitly name alternatives or say when not to use it, but the sibling context (get_lab, hera_facts) and the explicit 'near a ZIP code' scope make the usage context 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, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds real value by enumerating the returned fields, which is important since no output schema exists, though it says nothing about error behavior for an invalid or stale labRef.

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 that front-load the returned data and end with the calling instruction. Every clause carries information, with only minor redundancy against the schema's labRef description.

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 enumerates the returned fields, and annotations cover the safety profile; a two-parameter, zero-required tool is well served. A brief note on failure cases for an invalid labRef would make it fully 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 description coverage is 100%, so both labId and labRef are already documented in the schema, including the deprecation note. The description only restates that labRef comes from find_labs, adding little beyond the structured fields — baseline 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?

States a specific verb+resource (get details for one Hera partner lab) and enumerates the exact content returned: address, phone, hours, walk-in policy, at-home collection, visit instructions and prices. The 'Pass the labRef returned by find_labs' clause cleanly differentiates it from the sibling find_labs.

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 instructs to pass the labRef returned by find_labs, which establishes the correct call order after a search. It stops short of stating when not to use it or what to do if a labRef is unknown, but the usage context is 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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by saying the facts are 'in its own words', indicating the answers use Hera's own phrasing rather than an independent summary. It also reveals the breadth of content without contradicting any annotation.

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 front-loaded sentence that names the resource before listing all supported topics. Every listed item corresponds to a valid enum value, so there is no filler. It is long but appropriately so, given the tool covers many distinct fact categories.

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 one-parameter, read-only facts tool, the description covers the full range of available topics and the nature of the content. It does not describe the exact return format, but 'facts' sufficiently implies textual answers. The lack of an output schema is not a major gap here because the tool's behavior is straightforward.

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 sole parameter `topic` is fully enumerated in the schema, and the description mostly repeats those enum values in natural language. It does add readable labels like 'at-home collection' and 'post-vasectomy checks', but it does not define the topics or explain how to map user intent to each enum value. Since schema description coverage is 0%, the description provides only marginal semantic value beyond the enum.

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 identifies the resource as Hera Fertility facts and enumerates the full topic scope, so an agent can tell what the tool returns. It does not use an explicit verb like 'returns' or 'explains', but the content is unambiguous. It is also distinct from the sibling lab-lookup tools by nature.

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 the tool: whenever a user asks about Hera's services, pricing, process, results, privacy, or related topics. It does not explicitly state when not to use it or compare against find_labs/get_lab, but the topic list provides clear context for selecting it.

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