Convert an image (PNG, JPEG, BMP or GIF, max 200 KB) to a ZPL ^GF graphic command. Returns the ZPL plus measured dimensions and the physical print size at the chosen density. PDF input is REST-only (/v1/graphics).
| Name | Required | Description | Default |
|---|---|---|---|
| dpmm | No | Target printer density | |
| wrap | No | Wrap the ^GF in a printable ^XA...^XZ label with ^PW/^LL | |
| dither | No | Dither instead of hard threshold (photos) | |
| image_base64 | Yes | Base64-encoded image bytes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden and covers the key traits: accepted formats, the 200 KB input ceiling, the output variant (^GF), and the return payload (ZPL plus measured dimensions and physical print size at the chosen density). It does not disclose failure behavior for oversized inputs or whether images are resized, but the essential operational behavior is transparent.
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 zero filler: the first front-loads the core purpose with format and size constraints, the second packs the return contents and the PDF routing exception. 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?
With no output schema, the description correctly explains what is returned (ZPL, measured dimensions, physical print size). Input formats, the 200 KB limit, and the PDF exclusion are all covered, and the schema handles parameter semantics. Remaining gaps — error behavior on oversize input and resizing behavior — are minor for a converter of this simplicity.
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% — every parameter (image_base64, dpmm, wrap, dither) already has a meaningful schema description, so the baseline is 3. The description adds only marginal semantics by linking 'the chosen density' to the physical print size output, tying dpmm to the return value.
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?
States a specific verb ('Convert'), a resource ('an image'), and a precise output ('ZPL ^GF graphic command'), while naming supported formats (PNG, JPEG, BMP or GIF) and a size limit (max 200 KB). This clearly differentiates it from all siblings, none of which convert images to ZPL.
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?
Provides one explicit exclusion with an alternative: 'PDF input is REST-only (/v1/graphics)', which prevents an agent from sending PDF through this tool and routes it to the right path. However, it gives no broader guidance on when to prefer this tool over related siblings such as convert_zpl_dpi or zpl_preview, so the selection guidance is partial rather than comprehensive.
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.