zpl_validate

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool returns a structured diagnostics report as JSON and lists the categories of checks performed (unknown commands, parameter range errors, layout overflow). It also hints that results depend on label size. It does not explicitly declare read-only behavior, but the nature of a validator and the mention of returning a report strongly imply a non-destructive operation. This is adequate for a validation tool, though it could mention whether it modifies anything or requires authentication.

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 two sentences with no fluff. The first sentence front-loads the purpose and output, and the second adds a critical usage tip. Every word earns its place, and the structure is efficient and clear.

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 tool's moderate complexity (4 parameters, no output schema), the description is complete enough for an agent to call it correctly. It states the input (ZPL), the output (JSON report), and the dependency on size parameters. It does not describe the exact JSON structure, but that is not strictly required since the description says 'structured diagnostics report' and lists common error types. It also implicitly distinguishes from other validators via the tool name. Minor omissions like behavior on success are easily inferred.

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 coverage is 100% as all four parameters have descriptions in the schema, providing the baseline of 3. The description adds value by explicitly emphasizing the importance of the size parameters ('Pass the real label size — checks depend on it.'), which goes beyond the schema's terse descriptions of width_in and height_in. It informs the agent that these parameters materially affect the tool's behavior, making it more than a bare restatement.

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 'Lint/validate' and the resource 'ZPL', and specifies the output as a structured diagnostics report in JSON with examples of error types. It differentiates from sibling validators by explicitly naming ZPL, making it unambiguous which language it handles.

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 provides a clear, actionable usage instruction: 'Pass the real label size — checks depend on it.' This gives context on how to use the tool effectively, but it does not explicitly mention when to use this tool over sibling validators like epl_validate or tspl_validate, nor does it state exclusions. It implies usage for ZPL validation via the name and content, so it earns a 4 for clear context without explicit alternatives.

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