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

Annotations already declare this as a read-only, idempotent, non-destructive, open-world operation. The description adds meaningful behavior beyond that: results are real-time, come in chronological pages, and should be continued with page=next_page when slots is not empty. It does not discuss auth, rate limits, or errors, but the annotations lower the need for that.

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 front-loaded with what the tool does, then bullet-lists the valid parameter combinations, then explains returns and pagination. It is fairly long but well structured and every section adds needed information, especially because there is no output schema.

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 7-parameter, no-required-parameter read tool with no output schema, the description covers the important invocation modes, return fields, slot_id handoff to cup24_get_slot_times, booking_url, and pagination. An agent has enough context to call 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 coverage is 100%, so the baseline would be 3. The description adds useful semantics not in the schema: service_ids can be combined only for combinable lab tests and are priced as one visit, and clinic_id may be used with one service id to find that clinic's soonest availability.

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 and resource: it gets real-time availability and prices from private clinics on cup24.it. It also distinguishes itself from cup24_get_slot_times by indicating that returned slot_id values are for that sibling, and it makes the three main lookup modes 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?

It gives explicit selection guidance with 'Pass one of' followed by three concrete parameter combinations: service_ids + city, clinic_id, or doctor_id. This tells an agent how to invoke the tool for different intents and names the downstream tool for a slot's detailed times.

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, openWorld and non-destructive, so the safety profile is covered. The description adds meaningful context beyond them: the data comes from the cup24.it service page (i.e. a static snapshot, not live availability), which explains the freshness caveat and reinforces the split with cup24_get_availability.

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, both front-loaded: the return payload first, the alternative-tool routing second. The parenthetical enumerations are dense but each item (fasting, duration, contraindications, lab tests) earns its place by telling the agent what questions this tool can answer.

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?

There is no output schema, so the description carries the burden of describing the return value — and it does so concretely (FAQ topics, per-city price range, package test lists). Combined with the routing hint to the availability sibling, nothing needed to call this correctly is missing.

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?

Single parameter with 100% schema description coverage; the schema even tells the agent where to obtain the id (from cup24_search) and gives format examples. The description adds nothing about service_id, but with full schema coverage the 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?

States a specific resource (a service's cup24.it page) and enumerates exactly what is returned: FAQ fields, min/max price per major city, and lab tests for check-up packages. It also names the sibling tool it is not (cup24_get_availability), so an agent can distinguish it without inspecting either 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?

The final sentence explicitly routes the agent: use cup24_get_availability for real-time prices and dates, implying this tool is for static descriptive/price-range data. That is a clear alternative-and-condition, though the 'use this when' side is only implied rather than stated outright.

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 cover the read-only, idempotent, non-destructive profile, so the description's added value is in what is returned: the slot's day plus the next available days for the same clinic and doctor. The special lab-test case (a single walk-in window with an example string) is genuinely useful disclosure that annotations cannot convey.

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 the core purpose and followed by the lab-test edge case and the booking next step. The Italian example string is a little heavy but earns its place by showing the exact format an agent will encounter.

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 carries the burden of describing the return shape, and it does so adequately: a per-day list of bookable times, with the lab-test variant spelled out. It omits error/empty behavior, but nothing essential for a correct call is missing.

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 baseline is 3, but the description adds meaning beyond the schema: results are 'day by day', starting with the slot's day then the next available days. That clarifies what the `days` parameter actually counts against (available days, not calendar days), which the schema alone leaves ambiguous.

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: exact bookable times for a slot, day by day, and names the sibling (cup24_get_availability) that supplies the slot_id. It also anchors the behavior to the website's 'Scegli data e ora' picker, so an agent can distinguish it from the availability search 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?

Makes the follow-up relationship explicit ('slot from cup24_get_availability') and gives the next step for booking ('open the slot's booking_url'), which is a clear usage path. It does not state exclusions, such as what to do when no slot times are returned or when to prefer get_availability instead, but the implicit sequencing is strong.

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 read-only, idempotent, non-destructive and open-world, so the safety profile is covered. The description adds genuinely non-derivable behavior: the Italian-language input requirement and the meaning of the returned flags (package=true for check-up packages, combinable=true for lab tests bookable together). It stops short of describing match fuzziness or any result-count 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 purpose and the Italian-language constraint lead, followed by a short scannable id-to-sibling mapping and a compact example list. Every bullet carries routing or semantic information; nothing is restated from the schema or annotations.

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?

There is no output schema, so the description compensates by sketching the response shape (services[].id, clinics[].id, doctors[].id) plus the package/combinable flags. Combined with the single well-documented parameter and the sibling id mapping, an agent has everything needed to call this correctly and chain the 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 coverage is 100%, so the query parameter is already documented with an example, establishing a baseline of 3. The description adds value above that by specifying the language the query must be in and supplying five realistic query examples spanning service, package, clinic and doctor 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 and resource ('Search cup24.it by name') and enumerates the three entity types covered: healthcare services, clinics and doctors, with concrete sub-categories (exams, visits, lab tests, check-up packages). An agent can immediately distinguish this from a lookup-by-city or an availability call.

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 usage constraint ('Search in Italian') and routes the agent forward by mapping each returned id to the specific field of the specific sibling tool where it is consumed. It does not explicitly state when to prefer cup24_search_cities over this tool, though the field-based routing makes that largely self-evident.

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 openWorld, so safety is covered. The description adds genuine behavior: results are slugs, and zones are encoded in a specific hyphenated format (e.g. 'firenze-zona-campo-di-marte'), which the agent needs in order to interpret and reuse results. Return format nuance (partial matches, empty results) is not covered.

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 short blocks: purpose, workflow handoff, examples. Nothing is wasted and the most important information (what it returns and how to use it) is 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 single-parameter lookup tool with no output schema, the description supplies purpose, return type, and the exact downstream usage. The only minor omission is behavior on zero or multiple matches, which is not critical for correct invocation.

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 baseline is 3. The description earns extra credit by showing that the query accepts both plain city names and zone-style strings, with multiple examples ('firenze', 'milano', 'sesto fiorentino') that clarify the free-text format beyond the single schema example.

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?

Specific verb (Find) plus resource (Italian cities and zones covered by cup24.it) and the return value (slugs). This is distinct from cup24_search and cup24_get_availability, so an agent can tell it apart from siblings without reading 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?

Explicitly states the downstream step: use the returned slug as the 'city' argument in cup24_get_availability, and explains how zones narrow the search. It doesn't state when to prefer this over the sibling cup24_search, which keeps it below a 5.

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