Kaio

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

Annotations already declare the write/side-effect profile (readOnlyHint=false, idempotentHint=false, openWorldHint=true, destructiveHint=false), so the safety burden is partly lifted. The description adds genuinely new behavior: the business uses phone/email to confirm and remind, and start_time must be a value previously returned by kaio_free_slots. It does not cover failure modes such as a slot being taken between listing and booking.

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?

Three sentences, each earning its place: what the tool does, the format/source of the critical start_time parameter, and the contact-detail requirement plus the confirmation precondition. Front-loaded with purpose and 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 an output schema present, return values need no explanation, and annotations cover the safety profile. The description supplies the key preconditions and the source of start_time. The remaining gap is the meaning of notes and the service/business identifiers, which an agent must infer from the sibling tools.

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 0% across 7 parameters, so the description carries the burden. It usefully defines start_time (an ISO 8601 slot from kaio_free_slots) and adds a constraint the schema does not encode — that a phone or an email is required even though neither is in the required list. However, slug, service_id, name, and notes remain entirely unexplained, so it only partially compensates.

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 and resource ("Book an appointment at a Kaio business") with no ambiguity about what the call produces. It also names the sibling tool kaio_free_slots as the source of valid start_time values, so an agent can distinguish it from the read-only siblings without opening any schema.

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?

Gives an explicit precondition gate: "Only call this after the person has confirmed business, service, day, time and contact details." It also implies the correct sequencing behind kaio_free_slots. It stops short of naming explicit when-not-to-use cases (e.g., slot no longer available), so it is strong context rather than full routing.

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 safety profile is covered structurally. The description adds the shape of the payload (description, timezone, booking link, services with ids/duration/price), but says nothing about auth requirements, rate limits, or error behavior for an unknown slug. With annotations carrying the safety burden, a 3 is appropriate.

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?

One dense sentence with zero waste; the resource and its input source are front-loaded, and the downstream id dependency is attached at the end where it matters. Every clause 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?

An output schema exists, so return values need not be re-explained, yet the description still flags the one field that matters for chaining (service ids). Combined with the slug provenance, the agent has everything needed to call this tool correctly and use its result.

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 0%, so the single slug parameter is undocumented in the schema. The description compensates by stating its provenance ('from search_kaio_businesses'), which tells the agent where to obtain a valid value. It does not give format details, but the provenance is the operationally important part.

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 (retrieve) and resource (a Kaio business) and immediately enumerates the returned fields, so the agent knows exactly what it gets. It also names the sibling that produces the input (search_kaio_businesses), making it distinguishable from the other three tools without opening any schema.

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?

Clear contextual guidance: the slug comes from search_kaio_businesses, and the service ids it returns are what kaio_free_slots and booking need, which tells the agent where this sits in the call chain. It lacks an explicit when-not statement (e.g., don't call it if you already have ids), so it falls just short of 5.

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 establish readOnlyHint=true and openWorldHint=false, so safety is covered. Beyond that, the description discloses the date-window constraint and that results mirror the live booking page, which is genuinely useful behavioral context for trusting/using the output.

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 dense sentences with no filler, and the core purpose is front-loaded ahead of the format and range constraints. The phrase 'at most a couple of weeks' is informal and slightly vague, but the wording is otherwise efficient.

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?

An output schema exists, so return values need not be described, and annotations cover the read-only profile. What an agent needs for a 4-required-parameter call is mostly present except for the slug parameter, which is left to inference.

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 0% description coverage across 4 parameters, so the description has to carry them. It adds real meaning for service_id (from get_kaio_business), start_date/end_date (YYYY-MM-DD format) and the span limit, but says nothing about slug, leaving one parameter entirely unexplained.

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 resource and result: free time slots for one service of a Kaio business, and clarifies these are the same slots the public booking page shows. It names the sibling get_kaio_business as the source of the service id, so an agent can separate it from book_kaio_appointment without opening schemas.

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?

It provides a useful prerequisite (service id comes from get_kaio_business) and a practical calling constraint (at most a couple of weeks per call), but never states when to use this versus book_kaio_appointment or what conditions make it inappropriate. Usage is implied 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 write semantics (readOnlyHint=false, idempotentHint=false, openWorldHint=true), so the safety profile is covered. The description adds genuinely useful behavior: the async callback model, the mandatory contact detail, and the require-confirmation-before-invocation rule. It does not describe the response or error 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?

Dense and front-loaded: the distinguishing mechanic comes first, then parameter formats, then the confirmation requirement. Every sentence earns its place, though the inline backtick formatting and heavy clause stacking make it slightly harder to scan.

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 an output schema present, return values need not be explained, and the description covers the distinctive usage flow and non-obvious parameters. The remaining gap is minimal guidance on slug/service_id sourcing, but the get_kaio_business cross-reference covers discovery for the business.

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 0%, so the description carries the burden and does so for the tricky fields: `description` contents (job, address/area, sizes) and `preferred_time` as free text with examples, plus the phone-or-email requirement. The simple identifiers (slug, service_id, name) are left to their self-evident names.

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 (leave a request for a service) and, crucially, the mechanic that separates it from slot-based booking: no slot is chosen and the owner calls back. It references get_kaio_business for the on_request condition. It does not name the sibling book_kaio_appointment, so the contrast is inferential rather than explicit.

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?

Gives a clear condition for use (service marked on_request) and a hard precondition ('call this only after they say yes'), plus a required confirmation flow. It stops short of explicitly routing the agent to book_kaio_appointment when a slot-based path is available, leaving that comparison 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 and openWorldHint=false, so the safety profile is covered. The description adds useful context beyond them: what the two filters accept semantically (type of business/treatment vs. town, province or area) and what a result contains (name, slug, address, Google rating, website, booking link, services with prices). It does not discuss result limits or pagination, keeping it below 5.

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?

Three tight sentences, front-loaded with purpose, then parameter semantics, then the return shape. Every clause conveys information an agent needs; 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?

An output schema exists, so the return-field list is partly redundant, but it is short and harmless. For a zero-required-parameter, two-filter search with covering annotations, filter semantics and result contents are both addressed; only the optional/default behavior of the empty filters is left implicit.

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 0%, so the description must carry the parameter burden, and it does: it explains that `what` is the business type or treatment and `where` is a town, province or area, with concrete Italian-language examples for both. The only gap is that it does not mention that both default to an empty string / that filters are optional.

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 and resource plus a meaningful scope qualifier ('businesses that can be booked online with Kaio'), which tells an agent this is a discovery tool rather than a fetch-by-id or booking tool. It does not name any sibling explicitly, so it falls short of the 5 benchmark for sibling differentiation, but the scope qualifier does most of that work.

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?

Searching for businesses is implied by the verb and the two filter arguments, so an agent can infer the context. However, there is no statement of when to prefer this over get_kaio_business, or of any prerequisite, exclusion, or empty-filter behavior, and no sibling is mentioned.

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