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EcoMCP

EcoMCP is an eco-intelligent MCP (Model Context Protocol) agent designed to provide carbon footprint insights for Indian foods. It combines a FastAPI backend with MCP integration to allow AI systems or other clients to query carbon footprint data efficiently.


🚀 Features

  • Query carbon footprint data by food name, region, or food ID.
  • Supports partial matches for food names.
  • Handles single and combination of regions.
  • MCP integration using FastMCP and stdio transport.
  • API hosted locally using FastAPI.

🧩 Architecture Overview

  • Transport: stdio
  • API Host: Localhost (http://127.0.0.1:8000)
  • MCP Host: Perplexity
  • MCP Client: FastMCP (handles requests and communicates with FastAPI)
  • MCP Server: FastAPI-based backend providing carbon footprint data

Flow:

User Query → MCP Host → MCP Client → FastAPI Server → JSON Dataset → Client → Host → Response

🗂️ Dataset

The project uses a curated Indian Food Carbon Footprint dataset, originally sourced from Kaggle. It contains information on carbon footprints for various Indian foods along with their region(s) of origin.

Dataset Pre-Processing

  • Region Normalization: Multiple regions listed for a single food item were standardized into a consistent, alphabetically sorted, comma-separated format.
  • Unique ID Assignment: Each food item was assigned a unique identifier based on its region combination and order within that group.
  • Format: The dataset is stored in JSON format.

Key Columns

  • ID → Unique identifier for the food item
  • Food → Name of the food item
  • Region → Single or combination of regions (e.g., "North, West")
  • Category → Food type (e.g., Veg, Non-Veg)
  • Serving → Standard serving size
  • Carbon Footprint(kg CO2e) → Emissions in kg CO₂ per kg of food

🛠️ Tech Stack

  • Python 3.11
  • FastAPI (Backend API)
  • FastMCP (MCP client integration)
  • Transport: stdio
  • Cursor IDE for development

⚙️ Installation

  1. Clone the repository:
git clone https://github.com/npthinks/EcoMCP.git
cd EcoMCP
  1. Create a virtual environment using uv:
# Using uv
uv venv create .venv
uv venv activate

🏗️ Usage

Running the FastAPI Server

uvicorn main:app --reload
  • The server will run at http://127.0.0.1:8000/

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An eco-intelligent MCP (Model Context Protocol) agent designed to provide carbon footprint insights for Indian foods.

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