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

With no annotations, the description must convey behavior on its own. It discloses exact matching and case-insensitivity, which are important behavioral traits. However, it does not explain whether multiple rows can be returned, what happens when no row matches, or how results are ordered or limited.

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 with no filler. The key behavior, exact match on a column, is front-loaded, and the case-insensitive qualifier is appended efficiently.

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?

The tool is simple with only two parameters and no output schema, and the description covers its core purpose and matching semantics. Still, with no annotations, it leaves behavioral gaps around return cardinality, empty results, and edge-case handling.

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 clarify parameter roles. It does so implicitly by saying a column equals a value, mapping 'column' to the field name and 'value' to the lookup value, and adds case-insensitive semantics. It still lacks detail such as accepted column names or value formatting.

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' and description 'rows of the Xlifflane dataset where a column equals a value exactly' clearly identify the tool as an exact-match row lookup. It distinguishes itself from siblings like dataset_search and dataset_top by emphasizing exact equality, though it does not explicitly name a sibling.

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 provides a clear use condition: use this tool when you need rows where a column equals a value exactly, with case-insensitivity noted. It does not mention alternatives or exclusions, but the exact-match context strongly implies when this tool is appropriate.

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