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

No annotations are provided, so the description carries the burden. It discloses meaningful behavior: exact equality, case-insensitive matching, and returning rows. It does not explicitly state read-only behavior or error cases, but 'look up' strongly implies a safe retrieval.

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, direct sentence with no unnecessary words or repetition. It efficiently captures the tool's core behavior.

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?

There is no output schema, and the description only says rows are returned; it does not clarify whether one or multiple rows are returned, the row shape, ordering, or possible errors. This is adequate for a simple lookup but leaves some 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 provides only types and minLength, but the description explains the core semantics: a column is compared to a value for exact case-insensitive equality. It does not enumerate valid columns or value formatting, but the two parameters are adequately clarified.

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 the operation: retrieve rows from the Lanyardo dataset where a column exactly equals a value, with case-insensitivity. This distinguishes it from broader search or comparison tools.

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 clearly implies exact-match lookups and notes case-insensitivity, giving good context. It does not explicitly name dataset_search as the alternative for non-exact queries, but the contrast is reasonably clear.

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