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

With no annotations, the description carries the full behavioral burden. It discloses case-insensitive exact matching and that multiple rows ('The rows') may be returned, but it does not mention read-only status, result limits, output shape, or error behavior.

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 sentence with no filler, and the exact-match constraint is front and center before the case-insensitive nuance. It earns its place by adding semantics beyond the tool name.

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 two-parameter lookup the core semantics are present, and the description states rows are returned. However, with no output schema or annotations it still omits match cardinality, result limits, and whether any column is allowed, leaving some ambiguity for an agent.

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%, but the description maps both parameters: column is the field to compare and value is the exact comparison value, including the case-insensitive matching rule. It does not define valid column names or value formats.

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 clearly states the operation: retrieve rows of the Attestvio dataset filtered by exact column-value equality, with case-insensitivity noted. This distinguishes it from a general search, though it relies on the sibling name dataset_search rather than naming it.

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 word 'exactly' implies this is the right tool for exact-match lookups rather than fuzzy/partial search, so usage context is only implied. There is no explicit statement of when not to use it or a mention of dataset_search for non-exact matching.

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