aggregate_data_tool

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

No annotations exist, so the description carries full burden. It discloses the return format (scalar JSON with value, column, function) and lists supported functions. It does not cover edge cases (e.g., empty data) but is transparent about core 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?

Two sentences, no redundancy. Purpose is front-loaded, and every sentence adds necessary information (purpose, alias, return format, functions).

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 tool's simplicity (single column aggregation) and the presence of a full schema and output schema, the description is complete. It covers purpose, alias, return structure, and supported functions without gaps.

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 all parameters. The description adds value by explaining the result format and default function (sum), beyond what the schema provides.

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 exactly what the tool does: aggregate a single column using a function. It distinguishes itself from siblings like filter_data_tool or group_data_tool by specifying it's a shorthand for transform_data(operation='aggregate') and returns a scalar result.

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 implies usage via 'Shorthand for transform_data(operation='aggregate')', suggesting when to use the simpler form. However, it does not explicitly state when not to use it or compare with alternatives like group_data_tool for multiple columns.

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