Compatibility RISK analysis of ZPL code against a specific printer model (e.g. 'zebra-zd421'). NOT an emulator and never says 'it works': reports the model's language posture with evidence level and size-rule findings.
| Name | Required | Description | Default |
|---|---|---|---|
| zpl | Yes | Raw ZPL code | |
| model | Yes | Printer model slug, e.g. zebra-zd421 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does meaningful work here. It discloses that the tool performs risk analysis, does not emulate, never makes a definitive 'it works' claim, and reports evidence level and size-rule findings. It does not discuss side effects, rate limits, or failure modes, but the tool's analytical nature makes these less critical.
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 a single dense sentence that front-loads the core purpose and then adds important boundaries and output expectations. There is no filler, and every clause contributes 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 gives a useful high-level summary of what the tool reports, but with no output schema it leaves the exact return structure undefined. Terms like 'language posture' and 'size-rule findings' are not explained, so an agent may need to invoke the tool to discover the precise output format.
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 the two parameters. The description adds an example ('zebra-zd421') and frames the analysis, but it does not add meaningfully new semantic details beyond the schema.
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 uses a specific verb and resource: 'Compatibility RISK analysis of ZPL code against a specific printer model.' It also clearly distinguishes itself from an emulator and from validation tools by stating it 'never says it works.' This is more than enough for an agent to identify what the tool does.
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 establishes a clear context for use: assessing compatibility risk against a specific model. It also gives an explicit exclusion ('NOT an emulator') and a boundary ('never says it works'), which helps prevent misuse. However, it does not name alternative sibling tools or explicitly state when to use them instead.
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.