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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals case-insensitivity but does not state whether zero, one, or many rows are returned, whether results are paginated or truncated, whether the operation is read-only, or what happens when no match is found.

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, economical sentence with no filler. The core exact-match semantics are front-loaded, and the case-insensitive caveat is attached without cluttering the main point.

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 with no annotations and no output schema, the description leaves meaningful gaps: it is unclear if multiple rows can be returned, what the result shape is, and how this differs from dataset_search. The core semantics are present, but an agent would need to infer too much before calling confidently.

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 input schema provides no parameter descriptions (0% coverage), and the description only vaguely maps 'column' and 'value' through the phrase 'where a column equals a value.' It does not explain valid column names, value formatting, or edge cases, so it only minimally compensates for the schema gap.

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 identifies the resource (rows of the Medcontra dataset) and the operation (retrieve rows where a column equals a value exactly, case-insensitively). It aligns with the title's 'exact key' idea and implies a distinction from dataset_search, though it does not explicitly name the distinction.

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 exact-match and case-insensitive phrasing implies this tool is for precise equality lookups rather than fuzzy or broad search. However, there is no explicit guidance on when to use this tool versus dataset_search, dataset_top, or dataset_columns, and no when-not-to-use conditions.

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