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 usefully explains that grouping commas and currency are handled and that non-numeric rows are excluded and counted, adding context beyond the schema. This is helpful but does not cover edge cases like an empty column or return format.

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, efficient sentence that front-loads the core statistics and appends relevant data-cleaning notes. Every word earns its place; there is no redundant phrasing or filler.

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 there is no output schema and no annotations, the description covers the core functionality and data-cleaning behavior, which is essential. However, it does not specify the return structure (e.g., whether results are returned as a dict) or how to handle a column that is entirely non-numeric. These are moderate gaps for an otherwise simple tool.

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 coverage is 0% — the description never mentions the 'column' parameter directly. While it refers to 'a numeric column,' it does not explicitly state that the 'column' parameter specifies the column name, leaving the agent to infer this from context. For a tool with a single parameter, this is a significant gap.

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 a specific verb ('compute'), the resource ('numeric column of the Lanyardo dataset'), and enumerates the exact statistics (count, min, max, mean, median, sum). This clearly distinguishes it from sibling tools like dataset_columns, dataset_search, or dataset_top, which serve different purposes.

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 it should be used for numeric column statistics, but does not explicitly name alternatives or state when not to use it. An agent can infer the use case from 'numeric column,' but there is no direct guidance on choosing between this and sibling tools.

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