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

With no annotations, the description carries the full behavioral burden and does disclose a key non-obvious trait: the match is case-insensitive. It does not disclose whether multiple matching rows are returned, what the output shape is, or what happens on no match, leaving some important behavior inferred.

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, front-loaded sentence with no filler. It states the resource, the operation, and the matching rule efficiently without repeating what the title or schema already show.

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, the description gives the core semantics but relies heavily on inference. There is no output schema, no annotations, and no mention of return shape, cardinality, or match failure behavior, so an agent must guess several practical details before confidently invoking the tool.

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 description coverage is 0%, so the description must compensate. It does map the two parameters into a semantic relationship: rows where a column equals a value. It does not explain column naming conventions, accepted value formats, or how to reference columns, but the parameter names and the equational phrasing provide enough basic guidance.

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 states a clear verb and resource ('Look a row up by an exact key'), and the description identifies the Duesvo dataset and exact equality matching. It differentiates from dataset_search through the word 'exactly', though it does not name the sibling explicitly. Minor grammar ambiguity ('where a column equals a value') and singular 'row' vs plural 'rows' slightly reduce clarity.

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 tool should be used for exact-match lookups on a specific column/value pair. However, it does not state when to prefer dataset_search or another sibling, nor does it give any exclusions or alternative conditions.

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