Full health report for a ZPL label: sectioned findings (syntax, size/DPI, orientation, barcodes, fonts, graphics memory, job behaviour) with an honest score — sections that cannot be assessed say so and are excluded from the score.
| Name | Required | Description | Default |
|---|---|---|---|
| zpl | Yes | Raw ZPL code | |
| dpmm | No | ||
| width_in | No | ||
| height_in | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the output is sectioned, that a score is produced, that unassessable sections are explicitly flagged, and that they are excluded from the score. This is substantive transparency about output shape and limitations, though it does not explicitly state read-only behavior or potential side effects.
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, front-loaded sentence that delivers the core purpose, output structure, and scoring behavior without wasted words. Every clause carries relevant 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?
Given the absence of an output schema and annotations, the description explains the report structure and scoring policy well. However, it omits any guidance on parameter usage (especially dpmm/width_in/height_in) and does not provide alternative routing, leaving an agent with some ambiguity for a relatively complex tool.
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 25% (only 'zpl' is described). The description fails to clarify the meaning or role of dpmm, width_in, and height_in, even though 'size/DPI' hints at them. With such low schema coverage, the description needed to compensate for parameter semantics but does not.
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 gives a specific verb ('health report') and a clear resource ('ZPL label') and enumerates distinct analysis dimensions (syntax, size/DPI, orientation, barcodes, fonts, graphics memory, job behaviour). This clearly differentiates it from siblings like zpl_validate, zpl_preview, and zpl_command_help, which imply validation, rendering, or command reference rather than a full diagnostic report.
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 implies the tool is for holistic analysis and scoring of ZPL labels, but it never explicitly states when to prefer this over zpl_validate or zpl_preview, nor does it mention exclusions. The use case is inferable from 'Full health report' but not directly guided.
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.