group_data_tool

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

Without annotations, the description carries full burden. It explains the grouping behavior, return format, and lists aggregation functions. It does not cover edge cases like handling of nulls or performance limits, but for a straightforward grouping tool, the transparency is adequate.

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 concise with three sentences and a list. It front-loads the main purpose and immediately provides additional context, with no redundant or unnecessary words.

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 has an output schema and three parameters, the description covers the core functionality and return format. It could mention expected input data format or behavior with no aggregations, but overall it is sufficiently complete.

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 coverage is 100%, so baseline is 3. The description adds value by explicitly listing the available aggregation functions (sum, mean, etc.), which are not detailed in the schema. This enhances parameter understanding beyond the schema.

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 the tool groups data by columns with optional aggregations, specifies it is a shorthand for transform_data(operation='group'), and notes it returns one row per group. This effectively distinguishes it from sibling 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?

The description indicates it is a shorthand for transform_data, providing implicit guidance when to use it versus the more general transform_data. However, it does not explicitly contrast with aggregate_data_tool or other siblings, nor does it state when not to use this tool.

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