Detect the printer language (ZPL, EPL, TSPL, CPCL) used in raw label code. Returns a confidence tier (high/medium/low) and signal codes to guide debugging.
Detect which printer language raw label code is written in (ZPL, EPL, TSPL or CPCL). Heuristic: returns the language plus a confidence TIER (high/medium/low) and signal codes — not a probability.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Raw label code to classify |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool is heuristic, returns a confidence tier (high/medium/low) and signal codes, and explicitly states it does not return a probability. This gives the agent important expectations about output quality and format, though it doesn't elaborate on error handling or edge cases.
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 concise, with the core purpose front-loaded in the first sentence and a supplementary detail in the second. There is no fluff, and every word earns its place.
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 there is no output schema, the description appropriately explains the return structure (language, confidence tier, signal codes) and clarifies the heuristic nature. It covers the essential information an agent needs to interpret results, though it could mention how to handle ambiguous inputs or what signal codes mean in practice.
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% since the single parameter 'code' is well-documented as 'Raw label code to classify'. The description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate.
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 clearly states the tool's purpose: detecting the printer language (ZPL, EPL, TSPL, CPCL) from raw label code. It uses a specific verb ('detect') and enumerates the exact resource types, which distinguishes it from language-specific validators like zpl_validate or epl_validate.
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 use case: when you need to identify the language of raw label code. However, it does not explicitly mention alternatives or provide conditions for when to use this tool versus the sibling validation/preview tools. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.