QR Branding

Generate a QR code from a prompt (AI)

qr_generate_ai

Generate a QR code where the QR Branding LLM picks colors and shapes from a natural-language prompt. Best for "make me a luxury gold dotted QR for a coffee brand" style requests.

Input Schema

Name	Required	Description
`prompt`	Yes	Natural-language design brief. The engine's LLM will pick colors, shapes, and style hints from this. Example: "luxury gold dots for a coffee brand".
`content`	Yes	The text or URL the QR will encode.
`logoUrl`	No	Optional logo URL to embed at the center.
`fileFormat`	No	Output format. Defaults to "png".
`outputWidth`	No	Output width in pixels (raster) or viewport units (svg). 64-4096.
`stylePreset`	No	Optional style preset name (e.g. "minimal", "luxury", "playful"). Guides the LLM but does not override the design fully.
`outputHeight`	No	Output height in pixels (raster) or viewport units (svg). 64-4096.

Output Schema

Name	Required	Description
`format`	Yes	File format the engine rendered: png, svg, jpg, webp or pdf.
`content`	Yes	The text or URL the QR encodes.
`filePath`	No	PDF in the stdio package only: local temp file the PDF was written to.
`mimeType`	No	MIME type of the rendered file (the engine's contentType).
`sizeBytes`	No	Size of the rendered file in bytes, as reported by the engine.
`resourceUri`	No	PDF on the remote server only: URI of the embedded resource block that carries the file.
`aiExplanation`	No	The design model's explanation of its choices, when the engine returns one.
`safeModeApplied`	No	True when the AI design did not scan and the engine re-rendered it in safe mode.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": false,
+  "properties": {
+    "aiExplanation": {
+      "description": "The design model's explanation of its choices, when the engine returns one.",
+      "type": "string"
+    },
+    "content": {
+      "description": "The text or URL the QR encodes.",
+      "type": "string"
+    },
+    "filePath": {
+      "description": "PDF in the stdio package only: local temp file the PDF was written to.",
+      "type": "string"
+    },
+    "format": {
+      "description": "File format the engine rendered: png, svg, jpg, webp or pdf.",
+      "type": "string"
+    },
+    "mimeType": {
+      "description": "MIME type of the rendered file (the engine's contentType).",
+      "type": "string"
+    },
+    "resourceUri": {
+      "description": "PDF on the remote server only: URI of the embedded resource block that carries the file.",
+      "type": "string"
+    },
+    "safeModeApplied": {
+      "description": "True when the AI design did not scan and the engine re-rendered it in safe mode.",
+      "type": "boolean"
+    },
+    "sizeBytes": {
+      "description": "Size of the rendered file in bytes, as reported by the engine.",
+      "maximum": 9007199254740991,
+      "minimum": 0,
+      "type": "integer"
+    }
+  },
+  "required": [
+    "format",
+    "content"
+  ],
+  "type": "object"
+}

First observedOct 9, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false and destructiveHint=false, so the safety profile is covered. The description usefully signals that output is LLM-driven and therefore variable, but says nothing about auth needs, rate limits, or whether the generated asset is returned inline versus stored.

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, zero waste, and the core mechanism (LLM picks the design) is front-loaded before the usage example. Nothing repeats the schema or the title verbatim.

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?

With an output schema present, return values need no explanation, and all 7 parameters are covered by the schema. The description is sufficient for correct invocation, though a note on determinism or cost/latency of the LLM step would have made it fully complete.

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 every parameter including the prompt brief, stylePreset and dimension bounds is already documented in the schema. The description only restates the prompt-driven behavior and adds no format, default, or constraint detail beyond what the schema provides; baseline 3 applies.

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

Purpose4/5

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

States a specific verb (Generate) and resource (QR code) and adds a real differentiator: the branding LLM picks colors and shapes from a natural-language prompt. That implicitly separates it from the plain qr_generate sibling, but no sibling is named explicitly, so the distinction still requires inference.

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?

"Best for 'make me a luxury gold dotted QR for a coffee brand' style requests" gives a concrete positive usage cue with an example, which is clearer than typical implied usage. It stops short of saying when NOT to use it or naming the non-AI alternative for deterministic output.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All QR Branding tools Inspect

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