Terraform-MCP-Server

# Terraform MCP Assistant

A FastMCP-based server that provides natural language interface to Terraform operations. This assistant allows you to manage your infrastructure using simple English commands instead of remembering specific Terraform syntax.

Features

• Natural language processing of Terraform commands
• Execution plan visualization
• State inspection and management
• Infrastructure deployment and destruction
• Configuration documentation
• Automatic workspace validation
• Error handling and formatted output

Installation

1. Clone the repository:

git clone https://github.com/yourusername/terraform-mcp-server.git
cd terraform-mcp-server

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\\\\Scripts\\\\activate

3. Install dependencies:

pip install -r requirements.txt

4. Optional: Install Graphviz for plan visualization:

• Windows: Download from Graphviz Download Page
• Linux: sudo apt-get install graphviz
• macOS: brew install graphviz

Configuration

1. Set up environment variables (optional):

export TERRAFORM_WORKSPACE="/path/to/terraform/workspace"
export LOG_LEVEL="INFO"

2. Place your Terraform configuration files in the workspace directory.

Usage

1. Start the MCP server:

python src/main.py

2. Example commands:

• "Initialize the Terraform workspace"
• "What will change if I apply?"
• "Show me the current state"
• "Apply the configuration"
• "List all resources"
• "Destroy the infrastructure"

Project Structure

terraform-mcp-assistant/
├── docs/                  # Documentation
├── examples/             # Example Terraform configurations
├── src/                  # Source code
│   ├── handlers/        # Command handlers
│   ├── main.py         # Entry point
│   └── config.py       # Configuration management
├── tests/               # Test files
├── .env                # Environment variables (not in VCS)
└── README.md           # This file

Development

1. Install development dependencies:

pip install -r requirements-dev.txt

2. Run tests:

pytest

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request