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 does reveal important behavior: the match is exact and case-insensitive. It does not disclose whether multiple rows can be returned, how column names are matched, or any pagination or error behavior, but the core lookup behavior is adequately conveyed.

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 one compact sentence with no filler; it names the dataset, the matching condition, and the case-sensitivity rule. The title adds a helpful framing as a key lookup. The only minor issue is that the description is a noun phrase rather than a clearer directive, but it is still efficiently written.

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 with no output schema and no annotations, the description covers the essential calling context: dataset, column, value, and exact case-insensitive matching. However, it does not describe the return shape, the possibility of multiple matches, or how this tool relates to the search sibling, making it adequate but not complete.

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 the parameter names and string types with zero description coverage, so the description is the main semantic source. 'Where a column equals a value exactly' gives meaning to both parameters and adds the case-insensitive detail. It does not clarify whether the column name itself is case-insensitive or how special characters should be handled, but for two simple string parameters this is reasonable.

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 and description clearly state that the tool retrieves rows from the Rapportvo dataset by matching a column to a value exactly. The phrase 'equals a value exactly (case-insensitive)' gives a specific matching mechanism and helps distinguish it from the likely fuzzy dataset_search sibling. The wording is slightly awkward as a noun phrase rather than an imperative, but the intent is unambiguous.

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 the use case: when you need rows where a column exactly equals a value. However, it does not explicitly say when not to use it or mention alternatives such as dataset_search for partial or approximate matches, so the routing decision is left mostly to inference.

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