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

With no annotations, the description carries the full burden of behavioral disclosure. It does mention handling of grouping commas and currency, and that non-numeric rows are excluded and counted—useful edge-case behavior. However, it does not describe the output format, error conditions (e.g., all non-numeric values, missing column), or whether the result is a single object. This is partial disclosure, so a 3 is appropriate.

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 all key information—statistics, dataset name, formatting handling, and non-numeric exclusion—without any wasted words. It is front-loaded with the primary purpose and then adds behavioral nuances, making it both concise and structured effectively.

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 the simple one-parameter tool with no output schema, the description covers the essentials: what stats are computed, how formatting is handled, and how non-numeric rows are treated. It does not explicitly state the return format (e.g., object with fields), but the list of stats implies it. For a tool of this complexity, this is nearly complete, so a 4 is justified.

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 provides only the parameter name 'column' with a type string and minLength. The description adds critical meaning: it specifies that the column is from the MowRouteWorks dataset and must be numeric. It also hints at handling of formatting (commas/currency). This meaningfully compensates for the 0% schema description coverage, earning a 4.

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') and resource ('numeric column of the MowRouteWorks dataset'), and lists the exact statistics (count, min, max, mean, median, sum). It clearly distinguishes from sibling tools like dataset_columns (which lists columns) or dataset_row (which fetches rows) by its focus on summary statistics.

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 clearly implies use for obtaining summary statistics on a numeric column, but does not explicitly mention alternatives or exclusions. The context is clear enough—if you need column stats, this is the tool—but it lacks an explicit 'use X instead when...' statement. This earns a 4 for clear context with no exclusions.

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