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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds the ordering behavior ('in the order given') and the any-of matching semantics, but does not state that it is a read-only query, nor does it mention result limits, pagination, or side effects.

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?

It is a single, tight sentence with no wasted words, and it front-loads the resource and filter condition. The fragmentary phrasing and em-dash aside are efficient but slightly reduce readability compared to a clean full sentence.

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 filter tool with no output schema and no annotations, the description covers what is returned and the primary use case. It leaves out when to prefer alternatives, safety profile, and parameter constraints, so it is minimally complete but not fully self-contained.

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?

With 0% schema description coverage, the description must compensate. It explains that 'column' selects the column to filter and 'values' are the values to match, and notes that output follows the given order. It does not explain the string type, minimum/maximum item constraints, or that column must be a non-empty string.

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 identifies the specific dataset ('Contractor Lead Quotes') and the filtering condition ('column is any of the given values'), and signals the use case 'for X vs Y questions'. It stops short of naming a verb like 'compare' or distinguishing itself from siblings such as dataset_search, so it is clear but not sibling-differentiating.

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?

It implies when to use it by stating 'for X vs Y questions', which is a contextual usage hint. However, it does not explicitly say when to use this tool instead of alternatives like dataset_search or dataset_row, leaving the agent to infer.

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