design-style-mcp-server

Design Style MCP Server

A Model Context Protocol (MCP) server that provides design style intelligence for AI content generation. Ships with 30 curated design styles — each with color palettes, typography, mood tokens, visual directives, and negative prompts ready for image/video generation pipelines.

No AI inference — pure deterministic scoring against style characteristics.

Features

Tools

Tool	Description
recommend_style	Deterministic style recommendation based on brand context, campaign objective, target demographic, and season. Returns a top match with scoring reasoning and ranked alternatives.
get_style	Retrieve structured design tokens for a given style slug: name, description, color palette, typography, mood, visual directives, and negative prompt sections.

Included Styles (30)

academia, art-deco, bauhaus, bold-typography, botanical, claymorphism, cyberpunk, enterprise, flat-design, industrial, kinetic, luxury, material, maximalism, minimal-dark, modern-dark, monochrome, neo-brutalism, neumorphism, newsprint, organic, playful-geometric, professional, retro, saas, sketch, swiss-minimalist, terminal, vaporwave, web3

Each style includes a full prompt file with design philosophy, color system, typography rules, visual directives, and negative prompts — sourced from designprompts.dev.

Quick Start

Prerequisites

• Node.js 18+

Install

git clone https://github.com/luminarylane/design-style-mcp-server.git
cd design-style-mcp-server
npm install
npm run build

Run

# Development
npm run dev

# Production
npm start

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "design-style": {
      "command": "node",
      "args": ["/path/to/design-style-mcp-server/dist/index.js"]
    }
  }
}

How It Works

recommend_style

Given a brand description, campaign objective, target demographic, and optional season, the server scores all 30 styles against their characteristics (objectives, demographics, industries, moods, seasonal fit) and returns the top match with alternatives.

Input:  { objective: "product_launch", demographic: "gen_z", brand: "streetwear" }
Output: { style: "neo-brutalism", reasoning: "...", alternatives: [...] }

get_style

Given a style slug, returns structured tokens extracted from the style's prompt file:

Input:  { style: "cyberpunk" }
Output: { name, description, colors, typography, mood, promptAdditions, negativePrompt, reference }

Configuration

Env Var	Default	Description
DESIGN_STYLES_PATH	./design-styles (relative to package)	Override path to the design-styles data directory

Architecture

• Transport: stdio (standard MCP protocol)
• Data: 30 style prompt files + descriptions index (plain text, bundled)
• Scoring: Deterministic fuzzy matching — no AI inference, no network calls
• Caching: Prompt files are cached in-memory after first read
• Dependencies: Only @modelcontextprotocol/sdk and zod

Development

# Development server (auto-reload)
npm run dev

# Type check
npx tsc --noEmit

# Build
npm run build

Adding a New Style

• Add the slug to DESIGN_STYLE_SLUGS in src/styles.ts
• Add characteristics (objectives, demographics, industries, moods) to STYLE_CHARACTERISTICS
• Add a description line to design-styles/descriptions.txt
• Create a prompt file at design-styles/prompts/{slug}.txt

License

MIT