dataframe_groupby_aggregate

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

With no annotations, the description provides basic behavioral context (grouping and aggregation), lists allowed aggregate functions, but does not disclose edge cases, handling of missing values, or side effects.

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 one line for purpose and a list of parameters. It is front-loaded with the main action. Minor improvement could be better separation of purpose and details.

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 description lacks information about the output format or return value. Since there is no output schema, the description should at least hint at what the result looks like, but it does not.

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 0%, and the description thoroughly explains each parameter: csv_text as raw CSV with header, group_by as column name, value_column as numeric column, and agg as one of the listed functions. This adds significant value.

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 verb (group and aggregate) and the resource (dataframe columns). It distinguishes from sibling tools like dataframe_describe by specifying the grouping and aggregation action.

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 provides no explicit guidance on when to use this tool versus alternatives. The allowed aggregation functions are listed, but context on optimal usage or exclusions is missing.

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