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

Annotations only provide readOnlyHint=true; the description goes well beyond that by warning that layer `grain`/`measuredFor` may describe a province or region rather than the comune, that null values must be explained rather than read as zero, and that each field carries a provenance envelope. These are genuine behavioral caveats not present in the structured fields.

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 opening sentence is very long and enumerates the payload, but it is front-loaded with the core action and key, and the two follow-up sentences each carry distinct, actionable guidance (grain interpretation, provenance/null handling). Dense but nearly 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?

With no output schema, the description must carry the burden of describing the return shape, and it does so thoroughly (layers, provinceWellbeing, provenance envelope fields). It stops short of describing result granularity or error behavior for an invalid/unknown code, but is sufficient to call and interpret the tool.

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?

There is exactly one parameter with 100% schema description coverage, including the pattern and an example (075029), so the schema does the work. The description repeats the '6-digit ISTAT code' lookup key but adds no syntax or format detail beyond what is already documented.

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+resource: retrieves one comune's profile by 6-digit ISTAT code, and enumerates the returned payload (population, foreign residents, prices, tax eligibility, layers, provinceWellbeing). This clearly differentiates it from search_comuni (search) and list_datasets (catalog listing), so an agent can pick it without opening sibling 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?

The lookup key ('6-digit ISTAT code') is stated, which implies use when the code is already known, but the description never explicitly contrasts this with search_comuni or states when-not to use it. Usage is inferable rather than spelled out.

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?

readOnlyHint already establishes the safety profile, and the description goes further by explaining the servable flag: false means an unverified draft whose values must never be quoted. That is exactly the kind of non-obvious behavioral caveat an agent needs. It stops short of describing ordering or completeness of the listing.

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: the first front-loads what the catalog contains, the second states the use case. Nothing is padded, and the parenthetical glosses (vintage, servable) earn their space by disambiguating field meaning.

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 flags the draft caveat, which covers the essentials for a parameterless read tool. It does not mention result volume or ordering, a minor gap for a catalog endpoint.

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 tool takes zero parameters, so the baseline of 4 applies; there is nothing for the description to add or omit on this dimension.

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 names the resource precisely (the dataset catalog) and enumerates the fields it exposes, so an agent knows exactly what comes back. It is clearly distinct from the comuni-oriented siblings (get_comune, search_comuni) by resource, though it never states that contrast explicitly.

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?

"Use it to say what data exists and where a reader can see it" gives a clear, actionable context for invoking the tool. There are no exclusions or named alternatives, but with only two unrelated siblings there is little routing ambiguity to resolve.

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 read-only safety, so the bar is lower, and the description still adds real context: matching is case- and accent-insensitive, partial names work, and results are identifiers only with no figures. The one gap is that it does not describe ordering or how matches are ranked when truncated by limit.

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 sentences, zero filler, and the most load-bearing facts (what it returns, how matching works, the ambiguity caveat) all appear up front in the first sentence.

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 two-parameter lookup with no output schema, the description covers the return shape (identifiers only, no figures), the matching rules, and the routing to get_comune. An agent has everything needed to call it correctly and interpret results.

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 matching semantics for q beyond the schema text — case-insensitivity and partial-name matching — which is exactly the kind of behavior an agent needs to phrase queries correctly. Limit behavior is left entirely to 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?

Specific verb (look up) plus resource (Italian comuni) and it names exactly what comes back (6-digit ISTAT code, province, region). It also explicitly contrasts with the sibling get_comune by framing itself as the tool that supplies the code get_comune needs.

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 when to use it (to obtain the ISTAT code for get_comune) and adds a genuine decision rule for ambiguity: several comuni share a name, so check the province before choosing. Alternatives and edge cases are both covered.

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