Provides tools for interacting with Terraform Cloud API, enabling management and monitoring of workspaces, runs, and their statuses across organizations.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Terraform Cloud MCP Server what's the current run status for the production-infrastructure workspace?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol server providing Terraform Cloud API integration as tools for AI assistants.
This MCP server exposes four main tools for interacting with Terraform Cloud:
get_run_status: Get current run status and recent runs for a workspace
list_workspaces: List all workspaces in an organization
get_workspace_details: Get detailed information about a specific workspace
get_run_details: Get detailed information about a specific run by its ID
Related MCP server: Terraform MCP Server
npm install
npm run buildThe server reads your Terraform Cloud token from ~/.terraform.d/credentials.tfrc.json. Make sure this file exists with the following format:
{
"credentials": {
"app.terraform.io": {
"token": "your-terraform-cloud-token"
}
}
}Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"terraform-cloud": {
"command": "node",
"args": ["/absolute/path/to/tf-cloud-mcp-server/build/index.js"]
}
}
}Test your server with the MCP Inspector:
npx @modelcontextprotocol/inspector node build/index.jsCreate a .vscode/mcp.json file in your project:
{
"servers": {
"terraform-cloud": {
"type": "stdio",
"command": "node",
"args": ["/absolute/path/to/tf-cloud-mcp-server/build/index.js"]
}
}
}Get the current run status for a Terraform Cloud workspace.
Parameters:
workspaceName (string, required): Name of the workspace
organization (string, optional): Organization name (default: "urbanmedia")
Example:
Get the run status for workspace "capivara-dpa-importer"List all workspaces in a Terraform Cloud organization.
Parameters:
organization (string, optional): Organization name (default: "urbanmedia")
Example:
List all workspaces in urbanmedia organizationGet detailed information about a specific workspace.
Parameters:
workspaceName (string, required): Name of the workspace
organization (string, optional): Organization name (default: "urbanmedia")
Example:
Get details for workspace "capivara-dpa-importer"Get detailed information about a specific Terraform Cloud run by its ID.
Parameters:
runId (string, required): Run ID (e.g., "run-abc123")
Example:
Get details for run "run-abc123"tf-cloud-mcp-server/
├── src/
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript (generated)
├── package.json
├── tsconfig.json
└── README.mdnpm run dev # Watch mode for TypeScript compilationnpm run buildMIT
Get detailed information about a specific Terraform Cloud run by its ID
Inputs: runId (required). Returns: id, source, status, message, +7 more
Get the current run status for a Terraform Cloud workspace
Inputs: workspaceName (required), organization. Returns: recentRuns, workspaceId, currentRunId, workspaceName, +1 more
Get detailed information about a Terraform Cloud workspace
Inputs: workspaceName (required), organization. Returns: id, name, locked, vcsRepo, +4 more
List all workspaces in a Terraform Cloud organization
Inputs: organization. Returns: workspaces
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 4 tools
The tools are mostly distinct, with clear separation between workspace operations (list_workspaces, get_workspace_details) and run operations (get_run_details, get_run_status). However, get_run_details and get_run_status could potentially be confused since both relate to run information, though their descriptions differentiate them as 'detailed information' versus 'current status'.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., get_run_details, list_workspaces). The verbs 'get' and 'list' are used appropriately and predictably throughout the set.
With only 4 tools, the server feels thin for managing Terraform Cloud resources. While it covers basic read operations, the scope suggests more comprehensive management (e.g., create/update/delete operations) would be expected, making the count borderline for the domain.
The toolset is severely incomplete for Terraform Cloud management. It only provides read operations (get and list) with no ability to create, update, or delete workspaces, runs, or other resources. This will cause significant agent failures when attempting full lifecycle management.
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