Rescale ZPL between printer resolutions (203/300/600 DPI): coordinates, fonts, barcode module width and label size are scaled. Embedded ^GF/~DG image data is NOT scaled; the response says so instead of silently passing it through.
| Name | Required | Description | Default |
|---|---|---|---|
| zpl | Yes | Raw ZPL code | |
| source_dpi | Yes | ||
| target_dpi | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden and handles it well. It explicitly discloses that embedded ^GF/~DG image data is NOT scaled and that the response will say so instead of silently passing it through. This is valuable behavioral context beyond a generic rescale promise, though it does not describe the success-response format or other error paths.
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, front-loaded with the main action and supported resolutions, followed by the key caveat. There is no filler and no redundant repetition of schema fields.
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 core operation and the most important exception, making it adequate for basic invocation. However, with no output schema and no annotation support, it leaves the success return value unspecified and does not resolve the 152 DPI discrepancy or same-DPI behavior.
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 only 33%, since source_dpi and target_dpi only have enums and no prose descriptions. The description adds meaning by identifying them as printer resolutions and explaining what gets scaled, but it omits 152 from the parenthetical even though the schema allows it, and it does not clarify same-DPI or invalid-combination behavior.
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: 'Rescale ZPL between printer resolutions', then enumerates exactly what is scaled (coordinates, fonts, barcode module width, label size). This clearly distinguishes it from sibling ZPL tools, which are mostly preview/validate/explain operations.
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 intended use is implied: call this when ZPL needs to be rescaled from one printer DPI to another. However, it does not explicitly state when not to use it, name alternative sibling tools, or address what to do if the requested DPI combination is unsupported.
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.