Terrakube MCP Server

The Terrakube MCP Server bridges the gap between AI assistants and Terrakube’s infrastructure‑as‑code platform by exposing a rich set of workspace, variable, module, and organization operations through the Model Context Protocol. For developers who rely on AI agents to orchestrate cloud deployments, this server eliminates the need for custom SDK wrappers or manual API calls. Instead, a Claude‑style assistant can issue high‑level commands—such as “create a new workspace for the marketing team” or “update the database connection string”—and receive typed, validated responses that are immediately actionable.

At its core, the server implements a comprehensive API integration with Terrakube. Every operation is defined as an MCP tool, complete with strict input schemas and clear output structures. This type safety, achieved through TypeScript, guarantees that the AI client receives consistent data and that errors are surfaced with descriptive messages. The server also supports flexible configuration via environment variables, allowing teams to deploy it in CI/CD pipelines, on-premises servers, or cloud functions without code changes.

Key capabilities include:

• Workspace lifecycle management: Create, update, delete, retrieve, and list workspaces across organizations.
• Variable handling: Add, modify, delete, and inspect variables within any workspace, with support for HCL formatting and sensitivity flags.
• Module operations (not fully listed in the excerpt but implied by the feature set) that enable dynamic module provisioning and version control.
• Organization management for multi‑tenant setups.

Real‑world scenarios benefit from this tight integration. A DevOps engineer can ask an AI assistant to spin up a sandbox workspace for a new feature branch, automatically injecting environment variables and linking the appropriate VCS repository. A security analyst can have the assistant audit variable sensitivity across all workspaces, ensuring compliance with internal policies. Even non‑technical stakeholders can request a summary of workspace configurations, receiving structured JSON that can be fed into dashboards or further analysis.

Because the server adheres to MCP standards, it plugs seamlessly into existing AI workflows. Tools can be chained—creating a workspace first, then populating variables, and finally triggering a Terrakube run—all within a single conversational thread. This reduces context switching, speeds up onboarding for new team members, and allows AI assistants to act as true “infrastructure operators” rather than mere information retrievers.

In summary, the Terrakube MCP Server delivers a type‑safe, error‑robust, and highly modular interface to Terrakube’s capabilities. It empowers AI assistants to manage infrastructure lifecycle with the same confidence and precision that developers expect from native SDKs, while keeping configuration simple and integration friction low.