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

The annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context beyond that by disclosing the 200-code cap per dimension, the total_codes count, the upstream data source, and the no-auth requirement, all of which help an agent set expectations.

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 the action and resource, then provides an example, output details, use cases, and source in a compact three-sentence structure. Every sentence contributes distinct information without repetition or 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 and both parameters already documented, the description covers the remaining important context: where dataflow_id comes from, how results are capped, when to use the tool, and that no authentication is needed. It is complete for an agent to select and invoke this reference-structure tool 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 description coverage is 100%, so the schema already documents dataflow_id and version. The description adds a helpful example and source reference, but the main added semantic value is the example usage, not new parameter-level detail that the schema lacks.

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: 'Get the dimensions and valid codes for an ISTAT dataflow.' It gives a concrete example dataflow_id and names the dimension types returned (frequency, territory, measure), clearly distinguishing this structure-lookup tool from the broader dataflows and series.data siblings.

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 gives explicit use cases: interpret dimension labels returned by istat.data and build a narrower key filter. It references istat.dataflows as the source for dataflow_id, which provides helpful routing context, though it does not explicitly state when to prefer this over the sibling istat.reference.dataflows or istat.series.data.

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