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
tags:
  - building-mcp-track-consumer

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. Contributors: benjosaur, reuzed, MJ141592

Train your own function here https://autofinder.onrender.com

Demo: https://drive.google.com/file/d/1Z-PoQgvLaEBnhMk8nwrKOnRj8sOKz-QH/view?usp=sharing

Ig Social Media Post: https://www.instagram.com/p/DRsxbbWCl0m/?img_index=6&igsh=MWoxc3ZrOTM2ZWxhbA==

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