Render CPCL label code and return a PNG preview. The preview assumes 203 dpi and is an interpretation of the CPCL manual covering a core command subset — no physical printer validation; the response reports label and warning counts.
| Name | Required | Description | Default |
|---|---|---|---|
| cpcl | Yes | Raw CPCL code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It proactively states the 203 dpi assumption, the core-command-subset limitation, the lack of physical printer validation, and the label/warning counts in the response. This is strong transparency for a rendering tool.
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 core function and output are front-loaded, and the caveats are packed into a single efficient second sentence. Every clause adds useful information.
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 purpose, key rendering assumptions, limitations, and response summary, which is enough for an agent to decide whether and how to call it. It does not specify how the PNG is returned (e.g., base64 versus a URI), but the absence of an output schema makes this a minor gap rather than a blocker.
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% for the single 'cpcl' parameter, and the schema already identifies it as raw CPCL code. The description reinforces the CPCL context but does not add new parameter-level detail such as encoding, size limits, or accepted command syntax, so the baseline 3 applies.
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 ('Render'), a resource ('CPCL label code'), and a concrete output ('PNG preview'). It is clearly distinguished from sibling tools like cpcl_validate (validation vs. rendering) and epl_preview/zpl_preview/tspl_preview by naming CPCL explicitly.
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 makes the intended use case clear: when you have raw CPCL code and want a rendered preview as a PNG. It does not explicitly mention alternatives or exclusion conditions, such as 'use cpcl_validate for validation instead,' so it stops short of the full 5.
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.