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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains that non-numeric rows are excluded and counted, and that currency and grouping commas are handled. This goes beyond what the schema alone would tell the agent. It does not mention error handling or return format, but the read-only nature of a stats tool is reasonably inferred.

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, well-structured sentence that front-loads the list of statistics, then adds important handling details. There is no fluff or repetition. It efficiently communicates the core purpose and key behavioral nuances. Slightly more detail on parameter syntax would be welcome, but overall it is concise and organized.

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 simple tool with one parameter and no output schema, the description covers the essential aspects: what statistics are computed, how the data is cleaned (commas, currency, non-numeric exclusion), and the fact it targets a numeric column. It does not specify the exact return format (e.g., JSON object) or error handling, but given the simplicity, the description is fairly complete. An agent can likely invoke it correctly with the given information.

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 has a single required parameter 'column' with no description, so schema coverage is 0%. The tool description adds meaning by specifying that the column must be numeric, which helps the agent choose an appropriate column. However, it does not elaborate on the format or constraints of the column value (e.g., must be a string column name). The description partially compensates for the missing schema descriptions but could be more explicit.

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 computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Upkeepvo dataset. It uses a specific verb ('computes' implied) and names the exact resource, distinguishing it from siblings that deal with columns, rows, search, etc. The list of statistics leaves no ambiguity about what the tool does.

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 context about how data is handled (grouping commas, currency, excluding non-numeric rows) but does not explicitly state when to use this tool versus alternatives like dataset_top or dataset_search. It implies usage for numeric column statistics but lacks explicit guidance or exclusions. No sibling alternatives are mentioned.

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