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

Beyond the read-only annotation, the description specifies crucial behavioral traits of the data: it returns 'past openings, usual month and the expected next opening date, with a confidence level.' This explains the content and nature of the predicted data.

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 definition is exceptionally concise. It delivers highly complex functional logic—including specific temporal and pattern-matching criteria—in a short, two-sentence summary without any redundant phrasing.

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?

Despite the lack of an output schema, the description comprehensively explains the *nature* of the information returned (dates, confidence levels, past/future openings), providing the agent with all necessary context for successful use.

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?

With 100% schema description coverage, the description is not required to explain individual parameters. However, the Italian descriptions clarify the function of each filter (e.g., 'Filtra per testo') and results constraint ('Risultati'), adding necessary semantic context.

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 clearly defines the resource (calls) and the precise criteria for selection: recurring yearly, matching a specific body, and being opened at least two years ago. This specificity makes it stand out from general 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?

It provides a direct use case for the tool: 'Use it to tell a user when a closed call should reopen.' This gives the agent a clear operational trigger for tool selection.

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