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

With no annotations, the description carries the burden, and it does convey the core behavior: rows are ordered by a numeric column and can be returned highest or lowest. It does not disclose tie-handling, null/missing-value behavior, non-numeric column errors, or the default limit, leaving material behavioral gaps.

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 focused sentence that front-loads the core ranking behavior and adds a natural-language query example. No words are wasted.

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 ranking tool, the description plus schema cover the main call shape. However, without annotations or an output schema, an agent is left guessing about returned row shape, limit defaults, and error behavior, so the definition is only minimally 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 description coverage is only 33%, so the description must compensate for the undocumented 'column' and 'limit' parameters. It adds only that the column must be numeric, and does not explain the semantics or defaults of 'limit' or how 'ascending' maps to the 'highest/lowest' wording beyond what the schema already says.

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 identify a ranking operation on the EmployeeDeskHQ dataset, returning rows by highest or lowest value in a numeric column. It is specific enough to distinguish from dataset_row and dataset_search, though it does not explicitly name any sibling it is not.

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 the tool is for top/bottom row queries, giving a clear intended context. However, it does not state when to prefer dataset_top over siblings such as dataset_stats or dataset_search, and offers no exclusions.

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