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

No annotations exist, so the description bears full responsibility for behavioral disclosure. It adds useful behavior: matching is exact and case-insensitive. However, it does not state whether the operation is read-only, how results are returned, or what happens on no/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 sentence with no redundant phrases. It front-loads the resource ('rows of the Curtilo dataset') then the condition. Every word contributes.

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, the description covers the core operation, but lacks an output schema and behavioral details like return shape or error behavior. It also does not clarify which dataset's columns are valid or if multiple rows can be returned. These gaps make it minimally viable rather than 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?

Schema coverage is 0%, so the description must compensate. It connects the two parameters by explaining a column is compared to a value, and that the comparison is exact and case-insensitive. It does not specify whether the value or column strings require specific formatting, but the simple schema already provides the string type.

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 states the tool returns rows from the Curtilo dataset where a column matches a value exactly, and notes case-insensitivity. This clearly differs from a fuzzy search tool like dataset_search, though it does not explicitly name it. No explicit verb is present, but the title supplies 'Look a row up'.

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 row lookups, but does not state when to prefer it over dataset_search or other siblings. There are no explicit exclusions or alternative tool mentions. The 'exactly (case-insensitive)' wording hints at the niche but leaves routing to the agent's inference.

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