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Labelixa

by com.labelixa
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Detect printer language

language_detect
Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesRaw label code to classify

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dilYesDetected printer language
guvenYesConfidence level
karmaNoTrue when the input mixes languages
puanlarYesPer-language score
kanitlarNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "dil": {
+      "description": "Detected printer language",
+      "type": "string"
+    },
+    "guven": {
+      "description": "Confidence level",
+      "type": "string"
+    },
+    "kanitlar": {
+      "items": {
+        "properties": {
+          "kod": {
+            "type": "string"
+          },
+          "satir": {
+            "type": "integer"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "karma": {
+      "description": "True when the input mixes languages",
+      "type": "boolean"
+    },
+    "puanlar": {
+      "additionalProperties": {
+        "type": "integer"
+      },
+      "description": "Per-language score",
+      "type": "object"
+    }
+  },
+  "required": [
+    "dil",
+    "guven",
+    "puanlar"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover safety traits (readOnly, idempotent, non-destructive), and the description adds meaningful behavioral context: the detection is heuristic, returns a confidence TIER plus signal codes, and explicitly is not a probability. This manages expectations about output semantics beyond structured 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 deliver the core action, enumerates target languages, and frames the output model clearly. The heuristic caveat is front-loaded and every sentence earns its place without wasted phrasing.

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 single-parameter, non-destructive tool with an output schema, the description is nearly complete: it names inputs, outputs, and expected uncertainty behavior. The only slight gap is that 'signal codes' are mentioned without explanation, but the output schema is expected to carry that detail.

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 description coverage is 100%, so the schema already documents the single 'code' parameter as 'Raw label code to classify.' The description reinforces that the code is raw label text and links it to language detection, but it does not add new syntax or formatting details beyond the schema.

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?

States a specific verb and resource: it detects the printer language of raw label code, enumerating the possible outputs (ZPL, EPL, TSPL, CPCL). This clearly differentiates it from sibling validators and previewers, which assume a language rather than detect one.

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

Usage Guidelines3/5

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

The description implies the use case—classify an unknown raw label language—but never explicitly says when to use this tool versus alternatives like zpl_validate, tspl_preview, or explain_zpl. There are no when-not or alternative conditions, so usage guidance is only inferred from the stated purpose.

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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