Render EPL/EPL2 label code and return a PNG preview. The preview assumes 203 dpi and is an interpretation of the EPL2 manual covering a core command subset — no physical printer validation; the response reports label and warning counts.
| Name | Required | Description | Default |
|---|---|---|---|
| epl | Yes | Raw EPL/EPL2 code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden and does well: it discloses the 203 dpi assumption, the core-command-subset interpretation, the lack of physical printer validation, and the response's label/warning counts. It does not detail error behavior for malformed or unsupported EPL, but the stated interpretation implies such limitations.
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 tight sentences with no filler. The primary action is front-loaded, and the most important caveats (dpi, subset interpretation, no physical validation, response counts) are packed in efficiently.
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 single-parameter, preview-only tool with no annotations and no output schema, this description is complete. It gives the input type, the output format (PNG preview), key rendering assumptions, limitations, and what the response reports.
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?
The schema already documents 'epl' as raw EPL/EPL2 code with 100% coverage, so the baseline is 3. The description adds meaningful context by explaining that the input is interpreted under a 203 dpi, core-command-subset model, which helps the agent set expectations for what commands will render correctly.
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 states a specific verb and resource: 'Render EPL/EPL2 label code and return a PNG preview.' It clearly distinguishes itself from sibling validation tools by emphasizing previewing rather than physical validation, so an agent can understand its role among epl_validate, cpcl_preview, and 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 description clearly implies usage for EPL/EPL2 previews with the caveat that it is not physical printer validation. It does not explicitly name alternative tools such as epl_validate, but the context is sufficiently clear about what this tool is for and its limitation.
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.