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

With no annotations, the description carries the behavioral burden. It usefully discloses case-insensitive exact matching and that rows are returned, but it does not clarify the singular/plural ambiguity between the title ('a row') and description ('rows'), nor what happens for zero or multiple 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?

The description is a single compact sentence that conveys the essential behavior without filler. It is appropriately sized and front-loads the resource and matching semantics.

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 two-parameter lookup tool, the description covers the core behavior, but it leaves some operational ambiguity around whether one row or multiple rows are returned and how exactness interacts with case-insensitivity in practice. No output schema or annotations exist to fill these gaps.

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 has no parameter descriptions, so the description must add meaning. It relates 'column' and 'value' to the exact-match comparison and case-insensitivity, but it does not explain valid column references, value formatting, or matching edge cases.

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 'Look a row up by an exact key' names a specific action and resource, and the description clarifies that it returns dataset rows where a column exactly equals a value, case-insensitively. This clearly distinguishes it from broader search-like siblings such as dataset_search.

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 exact-match usage by emphasizing 'equals a value exactly (case-insensitive)', which hints at when to choose this tool over fuzzy search. However, it does not explicitly mention alternatives or state when not to use it, leaving the routing partly to inference.

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