query_export

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

With no annotations, the description fully discloses key behaviors: predicates are AND-combined, operators are listed with defaults (case-insensitive, inclusive date ranges), limit behavior (implicit 1000, limit=0 for total count), and return structure (rows, total_matched, truncated flag). No contradictions.

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?

Four sentences efficiently cover purpose, operators, projections, and limit/return details. Every sentence adds value without redundancy. Front-loaded with the main action.

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?

Given the complexity of predicates and limit options, the description covers all essential behavior. The output schema is noted but not needed to explain returns since the description already lists rows, total_matched, and truncated flag. No 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?

Schema description coverage is 0%, yet the description adds critical semantics: how filters combine, operator behaviors, default case-insensitivity, limit=0 behavior, and column projection. This goes well beyond what the raw schema provides, earning a top score.

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 identifies the tool as filtering rows from a NetSuite export using a list of AND-combined predicates. It specifies the resource (rows from export), the action (filter), and key details like operators and result. This distinguishes it from sibling tools like aggregate_export or get_headers.

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 explains when to use the tool (to filter rows) and provides context on predicates and projections. However, it does not explicitly state when not to use it or mention alternatives among siblings, so it falls short of a 5.

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