Terraform MCP Assistant

# 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

Related MCP server: mcp-server-terraform

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