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

With no annotations present, the description carries the behavioral burden and handles it well. It discloses that grouping commas and currency are parsed, and that non-numeric rows are excluded and counted. This gives the agent meaningful expectations beyond the tool's basic purpose, though it does not describe output structure or error 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 a single, dense sentence that front-loads the computed metrics and immediately follows with parsing/exclusion behavior. There is no filler, repetition, or unnecessary detail.

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?

For a one-parameter tool with no output schema and no annotations, the description covers the essential invocation context: what is computed, which dataset/column it applies to, and how messy data is handled. The only minor gap is the exact return format, but the listed metrics strongly imply the output contents.

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?

The schema only defines column as a non-empty string, so the description adds valuable meaning: the column refers to a numeric column in the Pickpathly dataset, and its values may contain currency/grouping formatting. This helps the agent understand what input is valid and how it will be interpreted.

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 tool's operation: computing count, min, max, mean, median, and sum for a numeric column of the Pickpathly dataset. This distinguishes it from sibling tools like dataset_search or dataset_row, and the title reinforces the exact resource and 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?

Usage is implied: call this tool when you need summary statistics for a numeric column. However, it does not explicitly state when to prefer this tool over siblings such as dataset_top or dataset_compare, and there are no exclusions or alternative conditions mentioned.

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