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

The description adds crucial contextual behavior that annotations miss, such as defining the temporal scope ('since 2021') and detailing the output complexity like 'a 6-month estimate with range.' This level of detail significantly improves the agent's understanding of the data's boundaries and predictive nature.

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 exceptionally concise, packing a wealth of data about the tool's function into a single, dense, and highly effective sentence. It front-loads the core purpose (time series) before listing the specific metrics available.

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?

The definition is highly complete. It covers the specific type of data, the temporal scope, the detailed contents of the time series (opened, closing, seasonality), and the optional filtering capability. It provides all necessary context for a successful call.

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?

Since schema description coverage is 100%, the baseline for this score is 3. The description successfully confirms the parameter's optional nature ('Optional region') but adds minimal semantic value beyond the structured definition.

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 explicitly defines the resource (Italian incentives catalogue) and the action (time series/trends), going beyond the simple name. It differentiates itself from other catalogue tools by specifying the inclusion of metrics like 'calls opened per month' and 'seasonality'.

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?

While it does not explicitly contrast itself with siblings like 'catalogue_stats,' the description's focus on 'time series' and '6-month estimate' clearly positions it for trend analysis rather than static lookups. This distinction acts as a strong usage guide for when this specific tool is appropriate.

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