designforyou

by org.swapp1990.designforyou

Generate design

generate

Generate one or more finished images from a template (get a template_id from recommend_template or browse_templates) plus a description. Use this after the user has selected or explicitly supplied a template_id; otherwise call recommend_template first so the visual gallery can collect the selection. Works for all categories (Instagram, logo, app-store, Visual Novel backgrounds/sprites, CG illustrations). Pass variants for multi-image output (expressions, time-of-day, etc.). Pass session_id to refine a prior result. Pass context_ids to ground a new image on prior designs (character consistency for VN CG scenes). Set model to 'minimax-h3-image-balanced' or 'minimax-h3-image-quality' (or use the 'h3 balanced'/'h3 quality' aliases) to render through the MiniMax H3 image service; context_ids are forwarded as ordered H3 reference images.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Optional image model override, such as grok-imagine-image-quality, gemini-3.1-flash-image, gpt-image-2, minimax-h3-image-balanced, or minimax-h3-image-quality. H3 aliases are 'h3 balanced' and 'h3 quality'.
`variants`	No	For character sheets: expression names e.g. ['neutral','smile','angry']. For backgrounds: time variants e.g. ['day','sunset','night']. For single images: omit or pass ['default'].
`edit_tier`	No	Quality tier when reference images (context_ids) are supplied. 'expert' routes the edit to gpt-image-2 for high-fidelity output; 'fast'/'pro' use Gemini. Ignored when no references are present.
`session_id`	No	Pass the session_id from a prior generate call to continue refining. The model will edit the previous image instead of generating from scratch.
`context_ids`	No	Design IDs of prior outputs to use as visual context. Use for CG scenes that should match a previously generated character.
`description`	Yes	What you want - describe the scene, asset, or design in as much detail as needed
`template_id`	Yes	Template ID from browse_templates
`context_labels`	No	Optional human labels for each context_id, in the SAME order. When the edit routes to a multi-reference model (gpt-image-2), an ordered legend ('reference image 1 = <label>, ...') is appended to the prompt so the model binds each named subject to its own reference instead of blending identities. Ignored for single-reference edits.
`reference_urls`	No	Optional ordered inline or HTTPS image references for direct image-model grounding. Use with reference_labels and reference_roles when supplying cast or location images.
`reference_roles`	No	Optional roles aligned with reference_urls, such as identity or location.
`reference_labels`	No	Optional labels aligned with reference_urls.
`skip_prompt_merge`	No	Skip the smart prompt-merge step (which compresses to ~150 words) and send the description through verbatim, with the template's base prompt concatenated for style. Use when the description is already complete and precise — e.g. a multi-character scene where each subject's outfit and a reference->subject legend must survive intact. Ignored on refines.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedAug 18, 2026

addedInput schema / properties / reference_labels

Added value: +{
+  "description": "Optional labels aligned with reference_urls.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 6,
+  "title": "Reference Labels",
+  "type": "array"
+}

addedInput schema / properties / reference_roles

Added value: +{
+  "description": "Optional roles aligned with reference_urls, such as identity or location.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 6,
+  "title": "Reference Roles",
+  "type": "array"
+}

addedInput schema / properties / reference_urls

Added value: +{
+  "description": "Optional ordered inline or HTTPS image references for direct image-model grounding. Use with reference_labels and reference_roles when supplying cast or location images.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 6,
+  "title": "Reference Urls",
+  "type": "array"
+}

Changed2 schema fields changedAug 18, 2026

changedInput schema / properties / model / description

Previous value: -"Optional image model override, such as grok-imagine-image-quality, gemini-3.1-flash-image, or gpt-image-2."New value: +"Optional image model override, such as grok-imagine-image-quality, gemini-3.1-flash-image, gpt-image-2, minimax-h3-image-balanced, or minimax-h3-image-quality. H3 aliases are 'h3 balanced' and 'h3 quality'."

addedInput schema / properties / model / enum

Added value: +[
+  "grok-imagine-image-quality",
+  "gemini-3.1-flash-image",
+  "gpt-image-2",
+  "minimax-h3-image-balanced",
+  "minimax-h3-image-quality",
+  "h3 balanced",
+  "h3 quality"
+]

Changed1 schema field changedJun 29, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}

First observedJun 24, 2026

TDQS

A4.8/5.0

Behavior5/5

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

The description discloses behavioral traits beyond annotations: it explains that session_id triggers refinement (editing previous image), context_ids provide visual context for consistency, and model selection with H3 aliases. It details how context_labels work for multi-reference models, and skip_prompt_merge bypasses compression. Annotations already show write intent (readOnlyHint=false) and non-destructive nature (destructiveHint=false), so the description adds rich operational context without contradiction.

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?

The description is three sentences with no wasted words. It front-loads the main purpose and then efficiently covers all major parameters and usage patterns. Every sentence earns its place by explaining a core aspect of the tool's behavior.

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?

Given 12 parameters and 100% schema coverage, the description provides a thorough workflow overview. It explains the required parameters, optional refinements, and model options. It could clarify the relationship between context_ids and reference_urls (are they mutually exclusive?) and note that context_labels only applies to gpt-image-2. However, it covers the core use cases well and the output schema exists to document return values.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds significant semantic context: explains that variants support expressions/time-of-day, context_ids ensure character consistency, and skip_prompt_merge retains detail for complex scenes. It also clarifies model aliases ('h3 balanced' == 'minimax-h3-image-balanced'). This goes beyond the schema's descriptions and helps the agent understand parameter interactions.

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 clearly states the verb 'generate' and the resource 'finished images from a template plus a description'. It distinguishes itself from sibling tools like recommend_template by explicitly stating the prerequisite of having a template_id, differentiating it from other generation tools. It covers a wide range of use cases across categories (Instagram, logo, etc.).

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

Usage Guidelines5/5

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

Provides explicit guidance: 'Use this after the user has selected or explicitly supplied a template_id; otherwise call recommend_template first'. Also explains when to use variants, session_id, context_ids, skip_prompt_merge, and model aliases. This directly helps the agent decide between this tool and its siblings.

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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