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 reveals important details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This goes beyond the bare operation and helps the agent understand edge-case handling.

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 with the key statistics front-loaded and the parsing details in a parenthetical. Every word earns its place; there is no unnecessary fluff.

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 one-parameter tool with no output schema, the description covers the input, the computed metrics, and edge-case handling. It does not explicitly state the output format, but the enumerated statistics make it obvious. Overall it is sufficiently complete for an agent to call the tool correctly.

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. It clarifies that the 'column' parameter refers to a numeric column in the PTOroom dataset, adding meaning beyond the plain string type in the schema. It does not fully explain naming conventions or error behavior, but the core parameter semantics are clear.

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 that the tool computes count, min, max, mean, median, and sum for a numeric column, which is a specific verb and resource. It distinguishes itself from sibling tools like dataset_search and dataset_top by focusing on summary statistics rather than lookup or row retrieval.

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 identifies when to use this tool: when summary statistics of a numeric column are needed. It does not explicitly mention alternatives or exclusions, but the purpose is stated directly enough that an agent can select it appropriately.

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