export_evaluation_set

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

No annotations provided, so description carries full burden. Only states export action without disclosing side effects, permissions, pagination behavior, or whether it's read-only. Minimal behavioral context.

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?

Single sentence without waste, but lacks structured breakdown of what the tool does versus parameter details. Adequately concise but incomplete.

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 3 parameters and no annotations, description fails to explain how parameters affect behavior or interpret output schema. Missing critical setup and usage context.

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%, and description provides no information about parameters (output_path, limit, token). Agent cannot infer their purpose or constraints from description alone.

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?

Description clearly states verb 'Export', resource 'extracted reaction steps', format 'JSONL', and explicit purpose 'for manual labeling and regression checks'. Distinguishes from sibling tools like compute_evaluation_metrics and get_evaluation_status by focusing on raw data export.

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?

Implies usage for manual labeling and regression checks, but no explicit when-to-use or when-not-to-use guidance relative to sibling tools like compute_evaluation_metrics or get_evaluation_status.

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