Terraform Cloud MCP Server

What does “Terraform Cloud” MCP Server do?

The Terraform Cloud MCP Server is a Model Context Protocol (MCP) server that integrates AI assistants with the Terraform Cloud API, enabling developers to manage their infrastructure through natural conversation. Built with Python and Pydantic models, this server is compatible with any MCP-supporting platform, including Claude, Claude Code CLI, Claude Desktop, Cursor, and Copilot Studio. By exposing Terraform Cloud functionalities as MCP tools, the server lets AI assistants perform actions like querying account details, managing workspaces and projects, and automating infrastructure tasks. This integration streamlines infrastructure-as-code workflows, making it easier for developers to interact with their cloud environments programmatically and conversationally.

List of Prompts

No prompt templates are mentioned in the repository.

List of Resources

No explicit MCP resources are described in the available documentation.

List of Tools

• Account Management: Enables retrieval of account details for authenticated users or service accounts.
• Workspace Management: Allows creation, reading, updating, deletion, and locking/unlocking of Terraform Cloud workspaces.
• Project Management: Supports creating, listing, updating, and deleting projects; managing project tag bindings; and moving workspaces between projects.

Use Cases of this MCP Server

• Account Overview: Retrieve and monitor details for the current Terraform Cloud user or service account, helping with access auditing and permissions management.
• Workspace Lifecycle Management: Create, read, update, and delete workspaces, enabling teams to automate setup and teardown of environments directly via conversational interfaces.
• Workspace Locking/Unlocking: Lock or unlock workspaces to prevent concurrent changes or enable maintenance, increasing operational safety.
• Project Organization: Create, update, and delete projects or move workspaces between projects, helping to maintain clear organizational boundaries within Terraform Cloud.
• Project Tag Management: Manage project tag bindings to support tagging strategies for cost allocation, compliance, or workflow automation.

How to set it up

Windsurf

1. Ensure Python 3.12+ is installed and Terraform Cloud MCP server is accessible.

2. Locate your Windsurf configuration file.

3. Add the Terraform Cloud MCP server to your mcpServers object:

   {
     "mcpServers": {
       "terraform-cloud": {
         "command": "python",
         "args": ["-m", "terraform_cloud_mcp"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.

5. Verify the server is connected and discoverable.

Securing API Keys
Use environment variables to set sensitive values. Example:

{
  "env": {
    "TERRAFORM_CLOUD_TOKEN": "your-api-token"
  },
  "inputs": {}
}

Claude

1. Ensure Python 3.12+ is available.

2. Download or clone the Terraform Cloud MCP repository.

3. In your Claude configuration (see CLAUDE.md), add:

   {
     "mcpServers": {
       "terraform-cloud": {
         "command": "python",
         "args": ["-m", "terraform_cloud_mcp"]
       }
     }
   }
   

4. Set your API token using an environment variable as above.

5. Restart Claude and verify the MCP server is listed.

Cursor

1. Install Python 3.12+ and clone the repository.

2. Open Cursor’s configuration settings.

3. Add the MCP server:

   {
     "mcpServers": {
       "terraform-cloud": {
         "command": "python",
         "args": ["-m", "terraform_cloud_mcp"]
       }
     }
   }
   

4. Use environment variables for secure API key storage.

5. Save and restart Cursor, then test the integration.

Cline

1. Download the Terraform Cloud MCP server and ensure Python 3.12+ is installed.

2. Edit Cline’s configuration file to include the MCP server:

   {
     "mcpServers": {
       "terraform-cloud": {
         "command": "python",
         "args": ["-m", "terraform_cloud_mcp"]
       }
     }
   }
   

3. Configure your Terraform Cloud API token using environment variables.

4. Restart Cline and verify operation.

Note: Always use environment variables for sensitive information like API keys.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "terraform-cloud": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “terraform-cloud” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

| Supports Roots | ⛔ | Not documented | | Supports Sampling | ⛔ | Not documented |

Our opinion

Based on the available documentation, Terraform Cloud MCP Server provides a focused set of infrastructure management tools and clear setup instructions, but lacks detailed descriptions of resources, prompt templates, or advanced MCP features like Roots and Sampling. It is well-suited for teams wanting to automate Terraform Cloud workflows via AI assistants but could benefit from richer MCP integration and documentation.

MCP Score