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

With no annotations, the description carries the full burden of disclosing behavior. It goes beyond simple 'computes statistics' by explaining that grouping commas and currency are handled, non-numeric rows are excluded and counted, and the output includes count, min, max, mean, median, sum. This provides concrete expectations about data cleaning and output content, which is valuable behavioral context. However, it doesn't disclose edge cases like empty columns or error behavior, but for a stats function with these details, a 4 is justified.

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 sentence that packs key information: the metrics computed, the dataset context, data cleaning behavior, and exclusion handling. It is concise but potentially dense; the information is front-loaded with the metrics list. It earns its place, though the sentence could be slightly unwieldy due to parentheses, but it's still efficient, so 4.

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 tool has one parameter, no output schema, and no annotations, with low complexity. The description covers the purpose, metrics, and data cleaning but omits specifics like output format (e.g., JSON structure), return value details, or edge cases (empty column all non-numeric). Since the output schema is absent, the description should clarify returns, but it only lists the stats without describing the response format有理. This leaves some gaps, but for a simple stats tool, it's adequate, so 3.

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 0%, so the description must compensate for the 'column' parameter. It mentions 'numeric column of the Background Check Quotes dataset', which indicates the parameter refers to a column name, but does not specify format, case sensitivity, or how column names are validated. For a single parameter with zero schema description, the description offers minimal meaning beyond 'column', so this is a significant gap, scoring 2.

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 the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column, clearly identifying the verb 'compute' and the resource 'numeric column'. It distinguishes from siblings like dataset_row (row retrieval) and dataset_search (search) by focusing on aggregation, though it doesn't explicitly name alternatives. This is clear but lacks explicit sibling differentiation, so 4 is appropriate.

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 the tool is for statistical analysis of a numeric column, and mentions that non-numeric rows are excluded and counted, giving context on data handling. However, it doesn't explicitly state when to use this tool versus alternatives like dataset_top (which might show top values) or dataset_compare (which might compare columns). The guidance is useful but implicit, warranting a 3.

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