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 reveals only the case-insensitive exact-match behavior and mentions 'The rows', implying multiple rows may be returned. It does not disclose whether all matching rows are returned, the output format, error behavior, ordering, or whether the column must exist in the dataset.

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 communicates the core behavior efficiently. It is front-loaded with the resource and includes the key matching constraints. It lacks any wasted words, though it could have included a brief note about return value without becoming bloated.

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 exact-match lookup tool, the description is adequate to make a basic call, but gaps remain. There is no output schema or annotations, and the description does not clarify the return shape (full rows vs. projections), whether multiple matches are allowed, or edge cases like no match. Given the sibling tools and the simplicity, it is minimally complete but not robust.

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 schema provides only parameter names and types with 0% description coverage. The description adds meaning by stating that 'a column equals a value exactly', which implies 'column' is the field to match on and 'value' is the comparison value. However, it does not elaborate on valid column identifiers, case-sensitivity of column names, or behavior when no match is found, 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 title states the action ('Look a row up by an exact key') and the description clarifies the resource ('RoughInDesk dataset') and the matching rule ('where a column equals a value exactly'). The phrase 'exactly (case-insensitive)' distinguishes it from the sibling dataset_search, which likely implies broader or fuzzy matching. It is clear but lacks a direct verb in the description itself.

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 implies this tool is for exact-match lookups by emphasizing 'equals a value exactly' and 'case-insensitive'. However, it does not explicitly state when to prefer this over dataset_search or any other sibling, nor does it provide exclusions. Usage must be inferred rather than explicitly guided.

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