estadistiques

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

With no annotations provided, the description carries full burden. It explains the aggregation operation, notes that field names are internal Socrata names, and advises using another tool to list fields. It does not discuss rate limits or authentication, but is transparent about the tool's behavior.

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 well-structured with clear sections, uses bold for emphasis, and front-loads the purpose. It is slightly lengthy but every part adds value. Could be trimmed slightly, but overall effective.

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?

Given the complexity of the tool, the number of siblings, and the presence of an output schema, the description is comprehensive. It covers common usage patterns, provides field mappings, examples, and guidance on parameters. No gaps are evident.

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 baseline is 3. The description adds significant value beyond the schema: it explains common fields per dataset, provides examples of usage, clarifies the relationship between operacio and camp_suma, and explains the dataset parameter. This is exemplary.

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 states that it generates aggregated statistics about contracts or subventions, provides specific field names and examples, and distinguishes this tool from sibling tools that focus on searching or detailing individual records.

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 clear guidance on when to use (for aggregated stats), recommends using 'llistar_camps' to check field names, and provides common fields per dataset. It does not explicitly state when not to use, but the context is clear.

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