What is Mcp Terraform Assistant

mcp-terraform-assistant is an MCP server designed for managing infrastructure as code using Terraform, enabling users to automate and streamline their infrastructure management processes.

Use cases

Use cases include setting up cloud resources like EC2 instances in AWS, managing multi-cloud environments, automating infrastructure deployment, and maintaining consistent infrastructure configurations across different environments.

How to use

To use mcp-terraform-assistant, you can either install it locally by cloning the repository and installing dependencies, or run it using Docker. After starting the server, you can interact with it using the MCP CLI commands to initialize, plan, apply, and manage your Terraform configurations.

Key features

Key features include initializing Terraform working directories, generating and showing execution plans, applying changes to infrastructure, destroying infrastructure, validating Terraform configurations, showing current state or saved plans, and managing Terraform workspaces.

Where to use

mcp-terraform-assistant can be used in various fields such as cloud infrastructure management, DevOps practices, and any environment where infrastructure as code is applied to automate and manage resources efficiently.

MCP Infrastructure as Code Assistant

An MCP server for managing infrastructure as code with Terraform.

Features

• Initialize Terraform working directories
• Generate and show execution plans
• Apply changes to infrastructure
• Destroy infrastructure
• Validate Terraform configurations
• Show current state or saved plans
• Manage Terraform workspaces

Prerequisites

• Python 3.8 or higher
• Terraform 1.5.7 or higher
• Docker and Docker Compose (optional)

Installation

Local Installation

1. Clone the repository:

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

2. Install dependencies using uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   uv pip install -e .
   

Docker Installation

1. Clone the repository:

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

2. Build and run the Docker container:

   docker-compose up -d
   

Usage

Local Usage

1. Start the MCP server:

   python main.py
   

2. Use the MCP CLI to interact with the server:

   mcp terraform_init --working-dir ./terraform
   mcp terraform_plan --working-dir ./terraform
   mcp terraform_apply --working-dir ./terraform --auto-approve
   

Docker Usage

1. Start the MCP server:

   docker-compose up -d
   

2. Use the MCP CLI to interact with the server:

   mcp terraform_init --working-dir ./terraform
   mcp terraform_plan --working-dir ./terraform
   mcp terraform_apply --working-dir ./terraform --auto-approve
   

Example Terraform Configuration

The repository includes an example Terraform configuration that creates an EC2 instance in AWS:

terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

provider "aws" {
  region = var.region
}

resource "aws_instance" "example" {
  ami           = var.ami_id
  instance_type = var.instance_type

  tags = {
    Name = var.instance_name
  }
}

Contributing

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

License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

• terraform_init: Initialize a Terraform working directory
• terraform_plan: Generate and show an execution plan for Terraform
• terraform_apply: Apply the changes required to reach the desired state
• terraform_destroy: Destroy the infrastructure managed by Terraform
• terraform_validate: Validate the syntax and internal consistency of Terraform files
• terraform_show: Show the current state or a saved plan
• terraform_workspace_list: List Terraform workspaces
• terraform_workspace_select: Select a Terraform workspace

Example Usage

Here’s an example of how to use the MCP server with an AI agent:

1. Start the MCP server:

   python main.py
   

2. Connect to the server using an MCP client:

   mcp connect http://localhost:8000
   

3. The AI agent can now help you with Terraform operations. For example:

   • Initialize a Terraform working directory
   • Generate and review execution plans
   • Apply changes to infrastructure
   • Destroy infrastructure resources
   • Validate Terraform configurations

Examples

Check out the examples directory for sample Terraform configurations that demonstrate how to use the MCP server:

• examples/aws-s3: A simple AWS S3 bucket example