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 state the core behavior (filter by column values, preserve order), but it omits any mention of output format, potential limitations (e.g., max rows, empty results), or that it is a read-only operation. This is a moderate gap given the lack of 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, compact sentence that conveys the essential purpose and the key behavioral detail (ordering). It is front-loaded with the main action and use case, with no superfluous wording. It could be slightly more structured but remains appropriately concise.

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 tool with only two parameters and no output schema, the description covers the filtering logic and order but leaves gaps: it does not specify the return format (rows as objects? arrays?), what constitutes a 'compare' result, or error handling for no matches. Given the simplicity, it is mostly adequate but not fully complete.

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 compensate. It explains the relationship between 'column' and 'values' (filtering by equality to any value) and that the order of values determines row order. However, it does not clarify details like case sensitivity, exact matching semantics, or what happens with duplicate values, leaving some ambiguity.

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 states a specific operation: retrieving rows from the Sacristo dataset filtered by a column matching any of the given values, preserving the order of those values. It also hints at the use case ('X vs Y' questions). While the verb is implicit, it is clear enough to distinguish from siblings like dataset_search (which likely does broader text search) and dataset_row (single row lookup).

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 usage hint, implying it is appropriate when comparing a few specific values side by side. However, it does not explicitly state when not to use it or name alternative tools, leaving the agent to infer the selection from the sibling names.

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