What is Tfmcp

tfmcp is an experimental command-line tool that enables AI assistants to manage and operate Terraform environments through the Model Context Protocol (MCP).

Use cases

Use cases include automating Terraform operations, analyzing infrastructure plans, and managing Terraform state with AI assistance.

How to use

To use tfmcp, install it via Cargo and run commands such as ‘tfmcp mcp’ to launch the MCP server, or ‘tfmcp analyze’ to analyze Terraform configurations.

Key features

Key features include deep integration with Terraform CLI, MCP server capabilities for AI assistants, high-speed processing, and automatic setup of sample Terraform projects.

Where to use

tfmcp is suitable for DevOps, cloud infrastructure management, and any environment where Terraform is used for infrastructure as code.

tfmcp: Terraform Model Context Protocol Tool

⚠️ This project includes production-ready security features but is still under active development. While the security system provides robust protection, please review all operations carefully in production environments. ⚠️

tfmcp is a command-line tool that helps you interact with Terraform via the Model Context Protocol (MCP). It allows LLMs to manage and operate your Terraform environments, including:

🎮 Demo

See tfmcp in action with Claude Desktop:

• Reading Terraform configuration files
• Analyzing Terraform plan outputs
• Applying Terraform configurations
• Managing Terraform state
• Creating and modifying Terraform configurations

🎉 Latest Release

The latest version of tfmcp (v0.1.3) is now available on Crates.io! You can easily install it using Cargo:

cargo install tfmcp

🆕 What’s New in v0.1.3

• 🔐 Comprehensive Security System: Production-ready security controls with audit logging
• 📊 Enhanced Terraform Analysis: Detailed validation and best practice recommendations
• 🛡️ Access Controls: File pattern-based restrictions and resource limits
• 📝 Audit Logging: Complete operation tracking for compliance and monitoring

Features

• 🚀 Terraform Integration
  Deeply integrates with the Terraform CLI to analyze and execute operations.

• 📄 MCP Server Capabilities
  Runs as a Model Context Protocol server, allowing AI assistants to access and manage Terraform.

• 🔐 Enterprise Security
  Production-ready security controls with configurable policies, audit logging, and access restrictions.

• 📊 Advanced Analysis
  Detailed Terraform configuration analysis with best practice recommendations and security checks.

• ⚡️ Blazing Fast
  High-speed processing powered by the Rust ecosystem with optimized parsing and caching.

• 🛠️ Automatic Setup
  Automatically creates sample Terraform projects when needed, ensuring smooth operation even for new users.

• 🐳 Docker Support
  Run tfmcp in a containerized environment with all dependencies pre-installed.

Installation

From Source

# Clone the repository
git clone https://github.com/nwiizo/tfmcp
cd tfmcp

# Build and install
cargo install --path .

From Crates.io

cargo install tfmcp

Using Docker

# Clone the repository
git clone https://github.com/nwiizo/tfmcp
cd tfmcp

# Build the Docker image
docker build -t tfmcp .

# Run the container
docker run -it tfmcp

Requirements

• Rust (edition 2021)
• Terraform CLI installed and available in PATH
• Claude Desktop (for AI assistant integration)
• Docker (optional, for containerized deployment)

Usage

$ tfmcp --help
✨ A CLI tool to manage Terraform configurations and operate Terraform through the Model Context Protocol (MCP).

Usage: tfmcp [OPTIONS] [COMMAND]

Commands:
  mcp       Launch tfmcp as an MCP server
  analyze   Analyze Terraform configurations
  help      Print this message or the help of the given subcommand(s)

Options:
  -c, --config <PATH>    Path to the configuration file
  -d, --dir <PATH>       Terraform project directory
  -V, --version          Print version
  -h, --help             Print help

Using Docker

When using Docker, you can run tfmcp commands like this:

# Run as MCP server (default)
docker run -it tfmcp

# Run with specific command and options
docker run -it tfmcp analyze --dir /app/example

# Mount your Terraform project directory
docker run -it -v /path/to/your/terraform:/app/terraform tfmcp --dir /app/terraform

# Set environment variables
docker run -it -e TFMCP_LOG_LEVEL=debug tfmcp

Integrating with Claude Desktop

To use tfmcp with Claude Desktop:

1. If you haven’t already, install tfmcp:

   cargo install tfmcp
   

   Alternatively, you can use Docker:

   docker build -t tfmcp .
   

2. Find the path to your installed tfmcp executable:

   which tfmcp
   

3. Add the following configuration to ~/Library/Application\ Support/Claude/claude_desktop_config.json:

If you’re using Docker with Claude Desktop, you can set up the configuration like this:

{
  "mcpServers": {
    "tfmcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-v",
        "/path/to/your/terraform:/app/terraform",
        "tfmcp",
        "mcp"
      ],
      "env": {
        "TERRAFORM_DIR": "/app/terraform"
      }
    }
  }
}

4. Restart Claude Desktop and enable the tfmcp tool.

5. tfmcp will automatically create a sample Terraform project in ~/terraform if one doesn’t exist, ensuring Claude can start working with Terraform right away. The sample project is based on the examples included in the example/demo directory of this repository.

Logs and Troubleshooting

The tfmcp server logs are available at:

~/Library/Logs/Claude/mcp-server-tfmcp.log

Common issues and solutions:

• Claude can’t connect to the server: Make sure the path to the tfmcp executable is correct in your configuration
• Terraform project issues: tfmcp automatically creates a sample Terraform project if none is found
• Method not found errors: MCP protocol support includes resources/list and prompts/list methods
• Docker issues: If using Docker, ensure your container has proper volume mounts and permissions

Environment Variables

Core Configuration

• TERRAFORM_DIR: Set this to specify a custom Terraform project directory. If not set, tfmcp will use the directory provided by command line arguments, configuration files, or fall back to ~/terraform. You can also change the project directory at runtime using the set_terraform_directory tool.
• TFMCP_LOG_LEVEL: Set to debug, info, warn, or error to control logging verbosity.
• TFMCP_DEMO_MODE: Set to true to enable demo mode with additional safety features.

Security Configuration

• TFMCP_ALLOW_DANGEROUS_OPS: Set to true to enable apply/destroy operations (default: false)
• TFMCP_ALLOW_AUTO_APPROVE: Set to true to enable auto-approve for dangerous operations (default: false)
• TFMCP_MAX_RESOURCES: Set maximum number of resources that can be managed (default: 50)
• TFMCP_AUDIT_ENABLED: Set to false to disable audit logging (default: true)
• TFMCP_AUDIT_LOG_FILE: Custom path for audit log file (default: ~/.tfmcp/audit.log)
• TFMCP_AUDIT_LOG_SENSITIVE: Set to true to include sensitive information in audit logs (default: false)

Security Considerations

tfmcp includes comprehensive security features designed for production use:

🔒 Built-in Security Features

• Access Controls: Automatic blocking of production/sensitive file patterns
• Operation Restrictions: Dangerous operations (apply/destroy) disabled by default
• Resource Limits: Configurable maximum resource count protection
• Audit Logging: Complete operation tracking with timestamps and user identification
• Directory Validation: Security policy enforcement for project directories

🛡️ Security Best Practices

• Default Safety: Apply/destroy operations are disabled by default - explicitly enable only when needed
• Review Plans: Always review Terraform plans before applying, especially AI-generated ones
• IAM Boundaries: Use appropriate IAM permissions and role boundaries in cloud environments
• Audit Monitoring: Regularly review audit logs at ~/.tfmcp/audit.log
• File Patterns: Built-in protection against accessing prod*, production*, and secret* patterns
• Docker Security: When using containers, carefully consider volume mounts and exposed data

⚙️ Production Configuration

# Recommended production settings
export TFMCP_ALLOW_DANGEROUS_OPS=false    # Keep disabled for safety
export TFMCP_ALLOW_AUTO_APPROVE=false     # Require manual approval
export TFMCP_MAX_RESOURCES=10             # Limit resource scope
export TFMCP_AUDIT_ENABLED=true           # Enable audit logging
export TFMCP_AUDIT_LOG_SENSITIVE=false    # Don't log sensitive data

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Roadmap

Here are some planned improvements and future features for tfmcp:

Completed

• [x] Basic Terraform Integration
  Core integration with Terraform CLI for analyzing and executing operations.

• [x] MCP Server Implementation
  Initial implementation of the Model Context Protocol server for AI assistants.

• [x] Automatic Project Creation
  Added functionality to automatically create sample Terraform projects when needed.

• [x] Claude Desktop Integration
  Support for seamless integration with Claude Desktop.

• [x] Core MCP Methods
  Implementation of essential MCP methods including resources/list and prompts/list.

• [x] Error Handling Improvements
  Better error handling and recovery mechanisms for robust operation.

• [x] Dynamic Project Directory Switching
  Added ability to change the active Terraform project directory without restarting the service.

• [x] Crates.io Publication
  Published the package to Crates.io for easy installation via Cargo.

• [x] Docker Support
  Added containerization support for easier deployment and cross-platform compatibility.

• [x] Security Enhancements
  Comprehensive security system with configurable policies, audit logging, access controls, and production-ready safety features.

In Progress

• [ ] Enhanced Terraform Analysis
  Implement deeper parsing and analysis of Terraform configurations, plans, and state files.

• [ ] Comprehensive Testing Framework
  Expand test coverage including integration tests with real Terraform configurations.

Planned

• [ ] Multi-Environment Support
  Add support for managing multiple Terraform environments, workspaces, and modules.

• [ ] Expanded MCP Protocol Support
  Implement additional MCP methods and capabilities for richer integration with AI assistants.

• [ ] Performance Optimization
  Optimize resource usage and response times for large Terraform projects.

• [ ] Cost Estimation
  Integrate with cloud provider pricing APIs to provide cost estimates for Terraform plans.

• [ ] Interactive TUI
  Develop a terminal-based user interface for easier local usage and debugging.

• [ ] Integration with Other AI Platforms
  Extend beyond Claude to support other AI assistants and platforms.

• [ ] Plugin System
  Develop a plugin architecture to allow extensions of core functionality.

License

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