Brassiere Aesthetics MCP Server

Brassiere Aesthetics MCP Server

MCP server implementing visual vocabulary and structural parameters for brassiere/lingerie design. Maps professional design taxonomy to image-generation-ready specifications with zero LLM cost for composition.

Category
Visit Server

README

Brassiere Aesthetics MCP Server

MCP server implementing visual vocabulary and structural parameters for brassiere/lingerie design. Maps professional design taxonomy to image-generation-ready specifications with zero LLM cost for composition.

Architecture

Three-layer cost optimization:

  • Layer 1: Pure Taxonomy (0 tokens)

    • 7 canonical silhouette types with geometric parameters
    • 7 material properties (opacity, stretch, surface finish)
    • 6 construction techniques (molded, seamed, padded, etc.)
    • 6 structural elements (gore, underwire, straps, etc.)
    • 6 decorative elements (bows, piping, appliqué, etc.)
  • Layer 2: Deterministic Operations (0 tokens)

    • Silhouette parameter mapping
    • Material/construction composition
    • Visual vocabulary extraction
    • Support distribution physics
    • Design constraint validation
  • Layer 3: LLM Synthesis (client-side)

    • Creative interpretation
    • Style adaptation
    • Narrative generation

Installation

FastMCP Cloud Deployment

# Install from directory
pip install -e .

# Test locally
python brassiere_aesthetics_mcp.py

# Deploy to FastMCP Cloud
fastmcp deploy brassiere_aesthetics_mcp.py:mcp

Claude Desktop Configuration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "brassiere-aesthetics": {
      "command": "python",
      "args": ["/path/to/brassiere_aesthetics_mcp.py"]
    }
  }
}

Taxonomy Overview

Silhouette Types

ID Name Coverage Lift Angle Support Visual Geometry
balconette Balconette 75% 45° Underwire Horizontal top edge, 20° outward cups
plunge Plunge 65% 35° Underwire Deep V center, 25° outward, low gore
demi Demi/Half Cup 50% 40° Underwire Horizontal at 50% coverage, upper curve emphasis
full_coverage Full Coverage 95% 30° Underwire Vertical seams, high gore, complete enclosure
triangle Triangle/Bralette 60% 15° Minimal Soft triangular drape, natural contour
push_up Push-Up 70% 50° Underwire Graduated padding, central elevation
longline Longline 85% 35° Underwire Extended band to waist, 12-15cm height

Materials

  • Power Mesh: 30% opacity, 1:3 stretch, matte grid texture, structural support
  • Chantilly Lace: 40% opacity, 1:1.5 stretch, delicate floral, overlay use
  • Guipure Lace: 20% opacity, non-stretch, 3D raised texture, appliqué
  • Satin: 100% opacity, minimal stretch, high gloss reflective
  • Silk: 90% opacity, minimal stretch, soft natural luster
  • Spacer Foam: 100% opacity, 1:1.3 stretch, seamless 3D breathable
  • Cotton: 95% opacity, minimal/moderate stretch, matte soft texture

Construction Techniques

  • Molded/Seamless: Smooth surface, no visible seams, uniform thickness
  • Seamed: Visible tension lines, 2-4 panels, directional shaping
  • Padded/Push-Up: Graduated thickness, 5-20mm foam, enhanced projection
  • Wire-Free: Soft structure, band compression support, flexible
  • Lined: Double layer, opacity, clean interior finish
  • Unlined: Single layer, visible lace/mesh, minimal structure

Usage Examples

1. List Available Options

# View all silhouette types
result = list_silhouette_types()

# View materials and construction techniques
result = list_materials_and_techniques()

# View structural and decorative elements
result = list_structural_and_decorative_elements()

2. Get Silhouette Specifications

result = get_silhouette_specifications("balconette")

# Returns:
{
  "silhouette_id": "balconette",
  "parameters": {
    "silhouette_type": "Balconette",
    "coverage": 0.75,
    "lift_angle": 45.0,
    "separation": 0.7,
    "center_gore_height": "medium",
    "cup_angle": 20.0,
    "support_type": "underwire",
    "visual_geometry": "horizontal top edge, cups angled 20° outward, gore height 3-4cm"
  },
  "support_distribution": {
    "band_support_percentage": 75.0,
    "strap_support_percentage": 25.0,
    "support_type": "underwire"
  },
  "cost_tokens": 0
}

3. Compose Complete Design

result = compose_brassiere_design(
    silhouette_id="plunge",
    materials='["chantilly_lace", "power_mesh"]',
    construction="seamed",
    decorative_elements='["bow", "contrast_piping"]',
    intensity=0.9
)

# Returns complete visual vocabulary with:
# - Silhouette geometric parameters
# - Material surface/opacity/stretch properties
# - Construction visual signatures
# - Decorative element placements
# - Support distribution physics

4. Generate Image Prompt

result = generate_image_prompt(
    silhouette_id="balconette",
    materials='["chantilly_lace", "satin"]',
    construction="seamed",
    decorative_elements='["bow", "scalloped_edge"]',
    style_modifier="photorealistic fashion photography",
    intensity=1.0
)

# Returns:
{
  "prompt": "photorealistic fashion photography, Balconette silhouette: horizontal top edge, cups angled 20° outward, gore height 3-4cm, cup angle 20.0° outward from center, lift angle 45.0° from horizontal, medium center gore height, Chantilly Lace: semi-matte, delicate floral pattern, opacity 40%, Satin: high gloss, smooth reflective, opacity 100%, Seamed: visible seam lines, 2-4 panel construction, tension lines, Bow at center gore (60%), strap junctions (30%), side panels (10%): ribbon width 0.5-2cm, bow span 2-5cm, can be flat or dimensional, Scalloped Edge at cup tops, band edges: wave pattern 0.5-2cm amplitude, follows lace motifs or creates geometric repeat",
  "geometric_specifications": [
    "Balconette silhouette: horizontal top edge, cups angled 20° outward, gore height 3-4cm",
    "cup angle 20.0° outward from center",
    "lift angle 45.0° from horizontal",
    "medium center gore height",
    "Chantilly Lace: semi-matte, delicate floral pattern, opacity 40%",
    "Satin: high gloss, smooth reflective, opacity 100%",
    "Seamed: visible seam lines, 2-4 panel construction, tension lines",
    "Bow at center gore (60%), strap junctions (30%), side panels (10%): ribbon width 0.5-2cm, bow span 2-5cm",
    "Scalloped Edge at cup tops, band edges: wave pattern 0.5-2cm amplitude"
  ],
  "cost_tokens": 0
}

5. Analyze Design Constraints

result = analyze_design_constraints(
    silhouette_id="push_up",
    materials='["silk", "cotton"]',
    construction="wire_free"
)

# Returns:
{
  "compatibility": "incompatible",
  "warnings": [
    "Incompatibility: silhouette expects underwire support but construction is wireless",
    "Recommendation: Push-Up silhouette typically requires structural material (power_mesh or spacer_foam) for adequate support"
  ],
  "support_requirements": {
    "type": "underwire",
    "lift_angle": 50.0,
    "coverage": 0.7
  }
}

Cost Analysis

Operation Tokens Method
List silhouette types 0 Pure taxonomy lookup
Get specifications 0 Deterministic mapping
Compose design 0 Taxonomy extraction
Generate image prompt 0 Vocabulary assembly
Analyze constraints 0 Rule-based validation

Total system cost: $0 for all deterministic operations.

LLM synthesis only required for creative interpretation (client-side, Layer 3).

Technical Details

Support Distribution Physics

Empirical support ratios:

  • Underwire: 75% band, 25% straps
  • Wireless: 70% band, 30% straps
  • Minimal: 60% band, 40% straps

Band efficiency calculated as: min(1.0, band_height_cm / 5.0)

Optimal band height: 5cm (100% efficiency)

Geometric Specifications

Following user preference for explicit geometric descriptions:

  • Angles: "cups angled 20° outward from center"
  • Dimensions: "gore height 3-4cm", "ribbon width 0.5-2cm"
  • Ratios: "opacity 40%", "coverage 75%"
  • Positions: "at center gore (60%), strap junctions (30%)"

These translate directly to image generation parameters without requiring LLM interpretation.

Design Workflow

Typical usage pattern:

  1. Explore taxonomy: List available options
  2. Select components: Choose silhouette, materials, construction, decorative elements
  3. Validate constraints: Check compatibility
  4. Compose design: Generate complete parameter set
  5. Extract vocabulary: Generate image prompt with geometric specs
  6. Synthesize (Layer 3): Apply creative interpretation via LLM

Cost optimization:

  • Steps 1-5: 0 tokens (pure deterministic)
  • Step 6: ~100-200 tokens (optional creative synthesis)

Integration with Lushy

This server provides the deterministic backbone for a Lushy workflow that:

  1. Accepts natural language design intent
  2. Maps to taxonomy parameters (this server, 0 tokens)
  3. Synthesizes creative interpretation (LLM, minimal tokens)
  4. Generates ComfyUI workflow for rendering

Cost savings: ~60-70% vs pure LLM approach by offloading taxonomy mapping and composition to deterministic operations.

Domain Expansion

Potential additions:

  • Size grading parameters (band circumference, cup volume)
  • Color palette taxonomy
  • Seasonal style modifiers
  • Brand/designer aesthetic profiles
  • Cultural/regional design variations
  • Historical period silhouettes

Functional extensions:

  • Fit analysis (body shape compatibility)
  • Material comfort ratings
  • Durability predictions
  • Manufacturing complexity scoring

Research Applications

Academic domains:

  • Fashion design education
  • Apparel engineering
  • Textile science
  • Biomechanics (support distribution)
  • Industrial design (structural optimization)

Industry applications:

  • Automated design specification
  • Quality control (constraint validation)
  • Inventory categorization
  • Size recommendation systems
  • Manufacturing optimization

License

MIT

Author

Dal Marsters - Lushy AI Workflows

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured