invariance_eval_dataset_append_example

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

Annotations already convey that this is a non-read-only but non-destructive operation. The description adds minimal behavioral context beyond the act of appending; it does not explain validation behavior, idempotency, ordering, or failure modes. It does not contradict the annotations, but adds little beyond them.

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?

A single sentence that front-loads the action, resource, and scope with no filler. Every word earns its place and the sentence is immediately digestible.

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 append operation, the combination of the concise description and the detailed schema provides enough information to invoke the tool correctly. The lack of output schema is acceptable since return values are not essential for an append operation, though error conditions are not described.

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 100%, and the body parameter is well-documented with required/optional fields and a concrete example. The description's mention of 'input + expected output' mirrors schema content, so it adds no significant meaning beyond what the schema already provides.

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 uses a specific verb ('Append') with a clear resource ('a single example row ... to an existing dataset') and specifies the row's composition ('input + expected output'). This clearly distinguishes it from dataset creation, listing, and seed-suite tools among the siblings.

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 phrase 'existing dataset' and 'single example row' imply this is for incremental additions rather than dataset creation or bulk seeding, but no explicit when-to-use guidance or alternatives are named. The usage context is inferable but not directly stated.

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