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

With no annotations provided, the description carries the behavioral burden. It states the selection rule (column value must be one of the given values) and output order (order given), making the retrieval behavior clear. It does not describe output row shape or error handling, but these are less critical for a simple filter tool.

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 dense sentence that front-loads the operation and dataset, adds the ordering detail, and ends with a practical usage tagline. There is no filler.

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 two-parameter read-only tool with no output schema, it covers the dataset, filter logic, ordering, and use case. It could be more explicit about exact-match semantics and whether full rows are returned, but nothing essential for invoking it is missing.

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 explain the parameters. It does: 'column' is the field to match, 'values' are the candidate match values, and the phrase 'in the order given' clarifies that value order controls result order. It meaningfully adds to the bare property names.

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 a concrete operation: retrieving rows from the FlatRateBook dataset that match any given values in a specified column, preserving the given order. It is clearly more specific than a generic 'compare', but it does not explicitly contrast itself with sibling tools like dataset_search or dataset_row.

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' gives a clear, practical trigger for when an agent should use this tool: comparing specific rows side by side. It does not name alternatives or state when not to use it, so it stops short of a full usage rule.

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