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

No annotations are provided, so the description must carry the full burden. It discloses valuable behavior: matching is 'any of' (OR semantics) and rows preserve the order of the given values. It does not explicitly state that the operation is read-only, nor mention limits or error behavior, but for a simple selection tool the stated semantics are sufficient.

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, dense sentence that states the operation, the target dataset, the filter semantics, the ordering rule, and the intended use case. No filler or redundancy.

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?

The description covers the core matching and ordering behavior, which is enough for a straightforward filter tool. It does not mention pagination, limits on the number of values, whether the match is case-sensitive, or how rows beyond the selected values are handled. Given the sparse schema and no output schema, those details would materially help an agent call correctly.

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 is the only source of parameter meaning. It defines column as a dataset column and values as the values to match, and the phrase 'in the order given' clarifies the ordering semantics of `values`. That adds real value beyond the bare schema.

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 specific dataset ('Sell My Business Quotes'), a concrete operation (select rows where the column is any of the given values), and a distinctive behavior (preserving the order of the given values). It also signals the comparison use case ('X vs Y' questions), which helps separate it from sibling tools like dataset_search or dataset_stats, though it never explicitly names a verb like 'return' or 'fetch'.

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?

It provides one clear usage context ('for X vs Y questions') which tells when to reach for this tool. However, it does not contrast with obvious siblings such as dataset_row or dataset_search, nor does it state when not to use it. The guidance is present but minimal.

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