Skincare

A custom Model Context Protocol (MCP) server that connects a Python recommendation engine to Claude. Built to explore NLP-based product matching and sequential decision-making for skincare.

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What It Does

Exposes two tools to Claude via MCP:

• find_similar_products — Vectorizes ingredient lists with TF-IDF and returns the top 5 most similar products by cosine similarity
• check_red_flags — Scans a product's ingredients for known irritants and flags them for sensitive skin users

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Architecture

skincare-mcp/
├── mcp_server.py            ← MCP interface (exposes tools to Claude)
├── engine.py                ← TF-IDF vectorization + cosine similarity
├── processor.py             ← Data loading, cleaning, fuzzy name matching
├── generate_user_history.py ← Synthetic RL interaction dataset generator
├── cosmetic_p.csv           ← Source dataset, 1884 Sephora products [not committed]
└── user_history.csv         ← Generated interaction logs [not committed]

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Technical Details

TF-IDF Ingredient Embeddings

Treats each product's ingredient list as a text document and vectorizes it with TfidfVectorizer from scikit-learn. Common ingredients like Water are down-weighted automatically while rare, distinctive ingredients receive higher weight. Similarity is computed via cosine similarity with bigram support for multi-word INCI names.

Fuzzy Product Name Matching

Uses thefuzz (Levenshtein distance) to resolve product names in three steps: exact match, partial match, then fuzzy match with a configurable threshold. Queries like "creme de la mer" (missing accent) resolve correctly.

Synthetic User History for Offline RL

Generates a structured interaction dataset to support Offline Reinforcement Learning:

Column	Description
user_id	Simulated user
timestep	Step in the user's skincare journey
dryness, acne, sensitivity, oiliness	State — skin concern levels (0.0–1.0)
product_name	Action — product applied at this timestep
reward	Reward — skin improvement score at T+1

The reward function accounts for product rating, skin type compatibility, and irritant penalties for sensitive users. The dataset structure is compatible with Batch-Constrained Q-learning (BCQ) and similar offline RL algorithms.

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Stack

Tool	Purpose
FastMCP	MCP server framework
scikit-learn	TF-IDF vectorization, cosine similarity
thefuzz	Fuzzy string matching
pandas / numpy	Data processing
uv	Package management

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Setup

Prerequisites: Python 3.11+, uv

git clone https://github.com/pserein/skincare-mcp.git
cd skincare-mcp
uv sync

# Download cosmetic_p.csv from Kaggle and place it in the project root
# https://www.kaggle.com/datasets/eward96/skincare-products-clean-dataset

.venv/bin/python generate_user_history.py

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "skincare-recommender": {
      "command": "/path/to/.venv/bin/python",
      "args": ["/path/to/skincare-mcp/mcp_server.py"]
    }
  }
}

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Roadmap

• MCP server with ingredient-based product similarity
• TF-IDF + cosine similarity for NLP-based matching
• Fuzzy product name resolution
• Synthetic user history dataset (State, Action, Reward)
• Offline RL policy (BCQ) trained on user history
• Skin-type filtering in similarity search

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Resume Description

Developed a custom MCP Server to bridge a Python recommendation engine with Claude. Engineered TF-IDF ingredient embeddings with cosine similarity for NLP-based product matching. Generated a synthetic sequential interaction dataset (State, Action, Reward) to support an Offline Reinforcement Learning policy using Batch-Constrained Q-learning (BCQ).