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

With no annotations, the description carries the full behavioral burden. It discloses the core selection rule and ordering behavior, which is good, but it does not describe what happens with missing values, duplicate values, or the exact shape of the returned rows.

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 entire description is one compact sentence with no filler. The essential behavior is front-loaded, and the use case is appended as a short contextual cue.

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 two-parameter tool with no output schema, the description provides enough information to invoke it correctly: choose a column, supply values, and expect rows in the given order. Additional detail about return format or edge cases would help, but the interface is simple enough that the description is largely sufficient.

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 0%, so the description must add meaning. It effectively explains that 'column' is the field to match against and 'values' is the list controlling which rows are returned and in what order. This compensates well for the sparse schema, though it does not name the parameters explicitly.

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 the operation: return dataset rows whose specified column matches any of the given values, in the order given. It distinguishes this from single-row lookup, free-text search, and aggregate/top tools, though it does not use an explicit verb like 'retrieve' or 'return'.

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 'for X vs Y questions' implies the intended scenario: comparing two or more specific rows identified by column values. However, it does not explicitly mention when not to use this tool or point to alternatives such as dataset_search or dataset_row.

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