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Labelixa

by com.labelixa
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Check printer compatibility

zpl_compatibility
Read-onlyIdempotent

Compatibility RISK analysis of ZPL code against a specific printer model, given as manufacturer/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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "zpl": "<zpl>",
      "model": "<model>"
    },
    "name": "zpl_compatibility"
  }
}

Input Schema

NameRequiredDescriptionDefault
zplYesRaw ZPL code
modelYesPrinter model slug, manufacturer/model, e.g. zebra/zd421

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / model / description
      Previous value: -"Printer model slug, e.g. zebra-zd421"New value: +"Printer model slug, manufacturer/model, e.g. zebra/zd421"
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds behavioral details beyond that: it never claims 'it works', and it reports 'evidence level and size-rule findings', which clarifies the output nature. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose and disclaimers. Every clause adds information without redundancy. Highly efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter tool with no output schema, the description adequately covers what the tool does, its constraints, and output highlights. It could elaborate on what 'evidence level' and 'size-rule findings' mean, but given the schema covers inputs, this is sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with descriptions for both parameters. The description adds a concrete example for the 'model' parameter format (manufacturer/model) and clarifies 'zpl' is raw code, but these are marginal additions over the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Compatibility RISK analysis'), the resource ('ZPL code against a specific printer model'), and provides a concrete example ('zebra/zd421'). It clearly differentiates from sibling tools like zpl_validate and zpl_preview by explicitly disclaiming emulation and 'it works' assertions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it (for compatibility risk assessment) and explicitly says what it is NOT (an emulator). It does not name specific alternative tools, but the 'NOT an emulator' clause guides the agent away from preview/validation use cases, which is a clear usage boundary.

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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