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

There are no annotations, so the description carries the full burden. It does not explicitly state that the operation is read-only, how ties are handled, what happens with invalid columns, or any other behavioral details beyond the basic ordering intent.

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 brief, directly to the point, and includes a helpful query-phrase example. No unnecessary words or redundant content.

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 description conveys the core purpose and the query pattern, but with no output schema it does not clarify the exact return structure, default limit behavior, or edge cases. It is adequate for a simple tool but leaves some contextual gaps.

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 low at 33%; only ascending has a description. The description adds that the column must be numeric but does not explain limit's default behavior or how it relates to the returned number of rows.

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 title and description clearly state the operation: ranking rows of a dataset by a numeric column and returning the highest or lowest values. This distinguishes it from sibling tools like dataset_search, dataset_stats, and dataset_row.

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 clear usage context with the 'which is the most/least X' pattern, but it does not explicitly mention when not to use this tool or compare it to alternatives such as dataset_search or dataset_stats.

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