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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that rows are returned in the order given, but does not disclose whether the operation is read-only, how it handles missing values, error behavior, or output format. This is a significant gap for a tool with zero annotation support.

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, concise sentence that front-loads the core behavior and ordering nuance. No unnecessary words or repetition. It is efficient while conveying the essential function.

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 filtering tool, the description covers the main semantics (column, values, ordering) and the intended use case. However, without annotations or an output schema, it lacks details about read-only nature, potential errors, and return structure, making it slightly incomplete for a fully informed agent call.

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 explains the semantics of both parameters: 'column' as the field to filter on and 'values' as the list of values to match. This adds meaning beyond the bare schema, which only defines types and constraints. However, it does not mention the array constraints (minItems=2, maxItems=10) or any format details, so compensation for the 0% schema coverage is only partial.

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 it returns rows from the dataset filtered by column values, and adds the ordering behavior. The phrase 'for X vs Y questions' implies a comparison use case, which helps distinguish it from siblings like dataset_search or dataset_row, but it does not name alternatives explicitly.

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 provides a specific usage context ('for X vs Y questions'), indicating when this tool is appropriate (comparing values). However, it does not explicitly state when not to use it or name alternative tools, leaving some inference required.

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