Lint ZPL code and return structured diagnostics: unknown commands, missing ^FS, out-of-range parameters, fields outside the label boundary. Run this before zpl_preview when generating ZPL.
| Name | Required | Description | Default |
|---|---|---|---|
| zpl | Yes | ZPL source | |
| dpmm | No | ||
| width_in | No | ||
| height_in | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the behavioral burden on its own. It states the tool returns structured diagnostics and lists what those diagnostics cover, making the read-only lint behavior clear. It could add output shape details, but the core behavior is adequately disclosed.
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?
Two sentences with no filler. The first sentence front-loads the action and output type, and the second gives actionable workflow guidance. Every word earns its place.
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 straightforward lint tool with no output schema and no annotations, the description provides selection cues, invocation timing, the required input, and expected output type. The missing parameter semantics and diagnostics shape are weaknesses, but the tool remains callable and appropriately contextualized.
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 only 25% (only 'zpl' is described). The description does not compensate by explaining dpmm, width_in, or height_in, their units, or how they determine the label boundary. This is a meaningful gap for low schema coverage.
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 ('Lint'), a specific resource ('ZPL code'), and enumerates concrete diagnostic checks (unknown commands, missing ^FS, out-of-range parameters, fields outside label boundary). This clearly distinguishes it from other-language validators and from zpl_preview.
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 instruction 'Run this before zpl_preview when generating ZPL' gives explicit when-to-use context and positions it in a workflow. It does not mention exclusions or alternatives like zpl_compatibility, so it stops short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Tools are largely organized by language and action, with clear pairs like cpcl_preview/cpcl_validate and zpl_preview/zpl_validate. The ZPL analysis tools (validate, explain, compatibility, command_help) have distinct purposes, though zpl_validate and explain_zpl overlap enough to cause occasional misselection.
Most tools follow a readable {domain}_{action} pattern such as zpl_preview, bulk_submit, and template_list, but there are several deviations: verb-first names like explain_zpl and convert_zpl_dpi, plus noun phrases like zpl_command_help and barcode_png. The mixed conventions are still understandable.
At 21 tools, this sits in the 16-25 'heavy' range, above the ideal 3-15 scope. The count is defensible given four label languages plus barcode, template, bulk, and conversion workflows, but it still feels dense for an agent to navigate.
The surface covers ZPL generation, validation, preview, compatibility, and conversion, plus validation/preview for CPCL, EPL, and TSPL, along with barcode, template, bulk, and language detection features. Minor gaps exist, such as no bulk job cancellation and no compatibility/health tools for non-ZPL languages, but core workflows have no dead ends.