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

With no annotations available, the description carries the full burden of behavioral disclosure. It states the core ranking behavior but says nothing about what is returned (full rows vs. values), default limit behavior, or tie/null 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?

A single sentence that is front-loaded with the core behavior and ends with a compact use-case phrase. It is concise and readable, though the quoted phrase partly restates the title.

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 tool is simple, with three straightforward parameters, so the description is functionally usable. However, with no output schema and no annotations, omitting the return shape and default limit leaves an agent with incomplete context for interpreting results.

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%, and the description adds the important constraint that column must be numeric, plus framing ascending as highest/lowest. It does not clarify limit defaults or provide further semantics for the limit parameter.

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?

States a specific operation: returning the highest or lowest rows of the Vatnix dataset ranked by a numeric column. The quoted use case 'which is the most/least X' clarifies intent, though it does not explicitly contrast 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 description implies when to use the tool through 'which is the most/least X', but it never states explicit conditions, exclusions, or alternatives among the sibling tools. An agent has to infer the decision boundary.

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