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Terraform-MCP-server
Este proyecto proporciona un servidor MCP (protocolo de contexto modelo) que expone operaciones de infraestructura de terraformación como código a través del lenguaje natural. Permite a los LLM ejecutar comandos Terraform y recuperar información sobre infraestructura sin requerir un conocimiento de sintaxis de comandos específico.
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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
- Clone the repository:
git clone https://github.com/yourusername/terraform-mcp-server.git
cd terraform-mcp-server
- Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Optional: Install Graphviz for plan visualization:
- Windows: Download from Graphviz Download Page
- Linux:
sudo apt-get install graphviz
- macOS:
brew install graphviz
Configuration
- Set up environment variables (optional):
export TERRAFORM_WORKSPACE="/path/to/terraform/workspace"
export LOG_LEVEL="INFO"
- Place your Terraform configuration files in the workspace directory.
Usage
- Start the MCP server:
python src/main.py
- 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
- Install development dependencies:
pip install -r requirements-dev.txt
- Run tests:
pytest
Contributing
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
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