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.