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

With no annotations, the description carries the burden of explaining behavior. It conveys a read-style ranking operation and notes that the column must be numeric, but it does not describe default ordering, tie handling, or whether full row objects are returned.

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 front-loaded sentence with no filler or redundancy. The illustrative "most/least X" wording makes the operation immediately understandable.

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 3-parameter tool, the description plus schema covers the high-level call pattern. It leaves some ambiguity about the returned format and default behavior, and no output schema exists to fill that gap, so the definition is adequate but not complete.

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 only 33%, and the description adds a meaningful constraint that 'column' must be numeric, which the schema does not state. However, it does not clarify the 'limit' parameter beyond its name, so it only partially compensates for the low schema coverage.

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 returns the highest or lowest rows of the HardFM dataset by a numeric column, making the core purpose concrete. It does not explicitly distinguish itself from sibling tools such as dataset_stats or dataset_search.

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 phrase "which is the most/least X" implies this is for extreme-value ranking queries, but there is no explicit guidance on when to prefer this tool over alternatives or when not to use it. The usage context is only implied, not stated.

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