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

The description discloses two behavioral traits: results are ordered according to the provided values, and the match is on 'any of' the values (an OR condition). However, it does not state whether the operation is read-only (no annotations are provided, so the description carries the full burden), nor does it mention response format, pagination, or error behavior. For a simple query tool this is acceptable but not thorough.

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 with no filler. It front-loads the primary purpose and includes the key qualifiers (column, values, order) without redundancy. Every part contributes to understanding the tool's function.

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 tool with no output schema and no annotations, the description covers the basic functionality but omits important contextual details: there is no mention of the dataset being 'Topcoatly' being the only dataset, no guidance on how this differs from dataset_row or dataset_search, and no statement about the read-only nature of the operation. An agent could call it correctly, but might misapply it without clearer sibling differentiation.

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 the semantic relationship between the two parameters: 'column' is the field to filter on and 'values' are the values to match. This adds meaning beyond the raw schema, which has no descriptions and 0% coverage. However, it does not elaborate on constraints (e.g., that values are strings, that the column must be an existing column) or provide examples, leaving some ambiguity for an agent.

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 what rows are returned (rows of the Topcoatly dataset filtered by column values) and that they are presented in the given order. It also frames the tool as serving 'X vs Y' comparison questions, which distinguishes it from generic search. However, it lacks an explicit action verb like 'returns' or 'retrieves', and does not directly contrast with any sibling tool by name.

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' provides contextual guidance on when to use the tool, but there is no explicit statement of when not to use it or which sibling tool to prefer (e.g., dataset_search for broader filtering). The guidance is implied rather than prescriptive, leaving the agent to infer the intended use case.

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