README.md

metadata

title: Utility Function Mcp
emoji: 💻
colorFrom: pink
colorTo: red
sdk: gradio
sdk_version: 6.0.1
app_file: app.py
pinned: false
license: mit

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

EV Utility Function MCP Server

An MCP (Model Context Protocol) server that provides utility function calculations for electric vehicles based on user preferences.

Features

This MCP server exposes two tools:

1. calculate_utility

Calculate the utility score for a single car based on a user's trained preferences.

Parameters:

• user_id: Username whose utility function to use
• price: Car price in euros
• range: Range in kilometers
• efficiency: Efficiency in Wh/km
• acceleration: 0-100km/h time in seconds
• fast_charge: Fast charging power in kW
• seat_count: Number of seats

Returns: JSON with utility score and coefficients used

2. find_best_car

Find the best car from an array based on a user's utility function.

Parameters:

• user_id: Username whose utility function to use
• cars: Array of car objects with the above features

Returns: JSON with the best car and all cars ranked by utility

Setup

Local Development

1. Install dependencies:

pip install -e .

2. Create a .env file with your Upstash Redis credentials:

UPSTASH_REDIS_REST_URL=https://your-redis-url.upstash.io
UPSTASH_REDIS_REST_TOKEN=your-token-here

3. Run the MCP server:

python server.py

Using with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ev-utility": {
      "command": "python",
      "args": ["/path/to/utility-mcp-server/server.py"],
      "env": {
        "UPSTASH_REDIS_REST_URL": "https://your-redis-url.upstash.io",
        "UPSTASH_REDIS_REST_TOKEN": "your-token-here"
      }
    }
  }
}

Hugging Face Space

This server is also available as a Hugging Face Space for easy web-based access and demonstration.

How It Works

1. User preferences (coefficients) are stored in Upstash Redis with key format params:{user_id}
2. The server fetches coefficients from Redis when calculating utilities
3. Features are scaled consistently with the training data
4. Utility is calculated as a dot product: utility = Σ(coefficient_i × scaled_feature_i)

Default Coefficients

If a user_id is not found in Redis, default coefficients are used:

• price: -0.5
• range: 0.8
• efficiency: -0.3
• acceleration: -0.5
• fast_charge: 0.6
• seat_count: 0.4