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

With no annotations, the description carries the full behavioral burden. It does disclose two meaningful traits — matching is exact and case-insensitive, and results are plural rows — but says nothing about result count limits, ordering, pagination, or what happens when no row matches.

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 front-loaded sentence with no filler; the dataset scope and the match semantics both land immediately.

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 simple 2-parameter lookup with no output schema, the description covers what the tool returns (rows), but leaves out how many rows come back, their ordering, and whether an unknown column name is an error — details an agent would want before calling it.

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% for the 2 required parameters, so the description must compensate. It maps the parameters semantically (column = which column, value = the exact value to match, matched case-insensitively) but does not say that 'column' must be an existing column name (the dataset_columns sibling) or give value format hints.

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 verb (look up rows), the resource (rows of the Yacht Charter Quotes dataset), and the precise predicate (column equals value exactly, case-insensitive). It is clearly distinct from a stats/top/columns tool, though it never names the sibling it is closest to (dataset_search) to sharpen the boundary.

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?

Usage is only implied: 'where a column equals a value exactly' signals this is for exact-match retrieval rather than the search/compare siblings, but there is no explicit when-to-use, when-not-to-use, or named alternative. The agent must infer the routing from the word 'exactly'.

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