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

With no annotations, the description carries the behavioral disclosure burden. It states the core ordering behavior (highest or lowest by numeric column) and implies a non-mutating query, but it does not mention default ordering, limit behavior, null handling, or return shape.

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 one compact sentence with a natural-language gloss. It is appropriately sized and avoids verbosity, though the phrasing could be slightly more direct.

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 is not complete for a tool with no output schema and low schema coverage: it omits limit semantics and default behavior, and gives no indication of what the returned rows look like. An agent could invoke it with just a column name but would be guessing about result size and format.

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 description adds important meaning by specifying that column must be numeric, which the schema does not convey, and clarifies the ascending concept via 'lowest/least'. However, it does not explain limit's role or default, leaving a gap given only 33% schema description 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 title and description together state a specific operation: rank/select the highest or lowest rows of the DrawScheduleWorks dataset by a numeric column. This clearly identifies verb, resource, and value semantics, though it does not explicitly contrast with sibling tools like 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 the intended use case for top/bottom ranking questions. However, there is no explicit guidance about when not to use this tool or when a sibling such as dataset_stats or dataset_search would be more appropriate.

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