Render TSPL/TSPL2 label code and return a PNG preview. The preview assumes 203 dpi and is an interpretation of the TSPL manual covering a core command subset — no physical printer validation; the response reports label and warning counts.
| Name | Required | Description | Default |
|---|---|---|---|
| tspl | Yes | Raw TSPL/TSPL2 code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden and does so well. It openly discloses the 203 dpi assumption, the interpreted/core-subset nature of rendering, the lack of physical printer validation, and the fact that the response reports label and warning counts. These are non-obvious behaviors an agent needs to know before invoking the 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?
The description is two tight sentences with the core action front-loaded and only high-value caveats following it. Every clause earns its place, with no filler, repetition, or vague language.
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?
For a single-parameter tool with no annotations and no output schema, the description is complete: it states the input, the output, the rendering assumptions, the limitation scope, and what the response reports. An agent has enough context to use the tool correctly and to set expectations about fidelity and return contents.
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?
The input schema already provides 100% coverage for the sole required tspl parameter. The description echoes 'TSPL/TSPL2 label code' but adds no additional syntax, formatting, size, or constraint guidance beyond what the schema states, so it stays at the baseline rather than adding 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?
The description opens with a specific action ('Render') and target resource ('TSPL/TSPL2 label code'), and clearly names the output ('PNG preview'). This differentiates it from validation-focused siblings like tspl_validate and from preview tools for other languages like zpl_preview, cpcl_preview, and epl_preview.
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 gives clear context for when to use this tool: to produce a visual preview of raw TSPL/TSPL2 code. It also states important limitations—203 dpi assumption, core command subset, and no physical printer validation—which help an agent decide not to use it for exact printer validation. It does not explicitly name an alternative tool like tspl_validate, so it falls just short of full routing guidance.
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.