delete_identical

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

The description explicitly states that the operation is irreversible, mutates the input dataset, and requires confirm=true. With no annotations provided, this full behavioral disclosure is critical and well-done.

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?

Three concise sentences efficiently convey purpose, usage, and behavioral traits without excess. Key information is front-loaded.

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 presence of an output schema, the description adequately covers what is needed: purpose, usage, behavior, and key parameters. It could be slightly more explicit about the need for a copied dataset, but the instruction 'use only on an intended working dataset' suffices.

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 reported as 0%, so the description must compensate. It mentions 'selected fields' and 'optional geometry tolerance' and notes that confirm=true is required, adding some meaning beyond the schema. However, it does not fully describe all parameters, such as the dataset path.

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 verb 'Delete duplicate rows', the resource 'feature class or table', and the method 'ArcPy DeleteIdentical' with key parameters. It distinguishes itself from sibling tools like delete_dataset or delete_field by focusing on row-level deduplication.

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 advises using the tool only on an 'intended working dataset', implying not on original data. This provides clear context for usage, though it does not explicitly list alternative tools for other scenarios.

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