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 does mention that rows are returned 'in the order given', which is a useful behavioral detail. However, it does not disclose side effects (e.g., read-only nature), error handling (e.g., missing column or no matching values), or output format, leaving significant ambiguity about the tool's runtime behavior.

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 primary function (extracting rows by column values) and then adds order behavior and use case. It avoids unnecessary verbosity and presents information in a logical flow, making it easy to parse. The structure is effective, though slightly dense.

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?

Given the lack of an output schema and annotations, the description is incomplete for full context. It does not clarify the output row structure (e.g., whether all columns are returned), or handle edge cases like no matches or invalid column names. It also does not mention how this tool fits relative to the sibling tools, leaving gaps in the agent's ability to anticipate results and make robust decisions.

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 that 'column' is used for matching (i.e., rows whose column matches any value) and that 'values' are the set of values to match, also implying order significance. This provides some semantic meaning beyond the schema's bare types. However, it does not specify details like exact vs. fuzzy matching, case sensitivity, or handling of duplicate values, which are important given the schema has no parameter descriptions (0% coverage).

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 that the tool returns rows from the TermsBird dataset where a specified column matches any of the given values, with results ordered by the provided value list. It explicitly mentions the intended use case ('for X vs Y questions'), making the core function unambiguous. However, it does not explicitly name the action verb (e.g., 'retrieve'), but the phrasing implies retrieval.

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 description offers minimal guidance on when to use this tool over siblings. It hints at a comparison scenario ('for X vs Y questions') but does not explicitly differentiate from tools like dataset_search or dataset_row. It lacks clear instruction on when this tool is preferred or how it relates to alternatives, leaving the agent to infer the selection criteria.

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