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

With no annotations, the description must carry the behavioral burden. It does disclose two key behaviors: matching rows if the column equals any supplied value, and preserving the given value order in the output. However, it doesn't mention return shape, pagination, or non-mutating guarantees, so transparency is partial.

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 packs in the operation, matching semantics, ordering behavior, and use case. Every phrase earns its place.

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 filtered-read tool with two parameters and no output schema, the description gives enough to invoke it correctly: dataset, column, values, ordering, and purpose. It leaves out explicit return-object details, but the title and 'rows' wording make the outcome reasonably clear.

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 coverage is 0%, so the description must explain the parameters. It does: 'column' is the column to match against and 'values' are the allowed values whose order determines the result order. That adds the relationship semantics missing from the schema, though it could be more explicit about value type constraints.

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 names a concrete operation: selecting rows of the Shortcodo dataset by matching a column against a set of values, in the supplied order. The 'X vs Y' framing and the title make its comparison intent clear and distinguish it from generic search or row-level siblings, though it doesn't explicitly name them.

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 'for X vs Y questions' note gives a useful when-to-use signal, but the description does not state when not to use it or how it compares with siblings like dataset_search or dataset_row. Usage is implied rather than explicit.

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