Render ZPL label code and return the label as a PNG image. Use this to SEE what ZPL output looks like — LLMs can write ZPL but cannot otherwise verify the visual result. One call renders one label (use index to pick a label from a multi-label stream).
| Name | Required | Description | Default |
|---|---|---|---|
| zpl | Yes | ZPL source (^XA...^XZ) | |
| dpmm | No | Printer density (8 = 203 dpi) | |
| index | No | Label index in the stream | |
| width_in | No | Label width, inches | |
| height_in | No | Label height, inches |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses that the tool returns a PNG image, renders one label per call, and supports selecting a label from a multi-label stream via `index`. This gives a solid behavioral picture, though it does not discuss error behavior for invalid ZPL or any limits.
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 filler. The core purpose is front-loaded, and the second sentence adds both the practical motivation and the important multi-label indexing behavior. Every clause 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?
The description covers the essential context: what it renders, what it returns, why to use it, and how indexing works. With no output schema, specifying PNG as the return format is especially valuable. It stops short of covering error cases or parameter interactions, but the overall guidance is sufficient for an agent to use the tool correctly.
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%, so the schema already documents all parameters. The description adds a useful mention of `index` for picking from a multi-label stream, but otherwise does not provide semantic details beyond what the schema already states.
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 opens with a specific verb and resource: 'Render ZPL label code and return the label as a PNG image.' It clearly states the tool's purpose and output, making it easy to distinguish from validation tools like zpl_validate or other language previews like epl_preview and cpcl_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 description gives a clear use case: use when you need to SEE what ZPL output looks like, since LLMs cannot otherwise verify the visual result. It also explains the one-call-per-label behavior with the `index` parameter, but it does not explicitly name sibling alternatives or situations where another tool should be preferred.
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.