What is Tibber Mcp

Tibber MCP is a Model Context Protocol (MCP) Server designed for Tibber, a Norwegian power supplier. It allows AI agents to interact with the Tibber API to access energy consumption data and pricing information.

Use cases

Use cases include analyzing power consumption patterns, querying historical energy usage, and optimizing energy costs based on real-time pricing.

How to use

To use Tibber MCP, install the required dependencies, clone the repository, and set up the server. Once running, connect via hosts like Claude Desktop or Roo Code to query energy data.

Key features

Key features include real-time access to energy prices, detailed power consumption analysis, and the ability to ask specific queries about energy usage patterns.

Where to use

Tibber MCP can be used in energy management systems, smart home applications, and AI-driven analytics tools that require integration with energy consumption data.

Tibber MCP Server

This is a Model Context Protocol (MCP) Server for Tibber, a Norwegian power supplier.

You can run the MCP server locally and access it via different hosts such as Claude Desktop or Roo Code.

For more details, check out my blog post:
Building a Tibber MCP Server: Connect Your AI Agent to Energy Consumption Data

Overview

The Tibber MCP server provides an AI agent with a convenient way to interact with the Tibber API and query information such as current energy prices and your energy consumption data.

Example Queries

Once connected to the MCP server, you can ask questions like:

• “Analyze my power consumption data and present the usual peak hours and any other interesting patterns in an easy-to-read format.”
• “When did I use the most power yesterday?”
• “How much power did I consume yesterday at 7 AM?”
• “What is the current energy price?”
• “List the 3 cheapest hours of tomorrow.”
• “Is the energy price higher or lower tomorrow?”

Demo video

Architecture

Requirements

• Python 3.12
• Tibber API token (You can get it from Tibber developer portal)

Installation

1. Install uv:

   # On macOS and Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

   # On Windows
   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   

2. Clone this repository:

   git clone https://github.com/linkcd/tibber-mcp.git
   cd tibber-mcp
   

3. Set up the Python virtual environment and install dependencies:

   uv venv --python 3.12 && source .venv/bin/activate && uv pip install --requirement pyproject.toml
   

Host Configuration

In Claude Desktop or Roo Code in VS

{
  "mcpServers": {
    "tibber": {
      "command": "uv",
      "args": [
        "--directory",
        "[PATH-TO-ROOT-OF-THE-CLONED-TIBBER-MCP-FOLDER]",
        "run",
        "server.py"
      ],
      "env": {
        "TIBBER_API_TOKEN": "[YOUR-TIBBER-TOKEN]"
      }
    }
  }
}

IMPORTANT: Replace [YOUR-TIBBER-TOKEN] with your actual token. Never commit actual credentials to version control.

Debug and test the MCP server locally

Run the server locally and run MCP inspector against it

npx @modelcontextprotocol/inspector -e TIBBER_API_TOKEN=[YOUR-TIBBER-TOKEN] python server.py

Available Tools

The server exposes the following tools that LLM can use:

1. get_consumption_data(): Get the hourly consumption data for the last 30 days, such as time period, total cost, base energy cost, and consumpted kwh
2. get_price_and_home_info(): Get price info (current, today and tomorrow) and home info (owner, address, subscription…)

License

MIT License

Acknowledgments

• This tool uses Anthropic’s MCP framework
• Built with FastMCP for server implementation
• The tibber ingeratoin is based on pyTibber library