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Terrakube MCP Server: Enterprise AI Deployment & Scaling - MCP Implementation

Terrakube MCP Server: Enterprise AI Deployment & Scaling

Terrakube MCP Server: Enterprise-grade Model Context Protocol powerhouses for seamless AI deployment, scaling, and governance across hybrid clouds.

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About Terrakube MCP Server

What is Terrakube MCP Server: Enterprise AI Deployment & Scaling?

Terrakube MCP Server acts as a central hub for managing AI workflows at scale. Designed for enterprise environments, it streamlines operations like workspace provisioning, variable management, and module orchestration—all critical for deploying complex AI models. Think of it as the nervous system for your AI infrastructure, ensuring everything from version control integrations to organizational policies runs smoothly under the hood.

How to use Terrakube MCP Server: Enterprise AI Deployment & Scaling?

  1. Setup the foundation: Configure API endpoints and authentication tokens via environment variables. This creates a secure bridge between your development environment and Terrakube's backend.
  2. Deploy with precision: Use workspace management commands to spin up environments tailored for specific AI projects. Optional VCS integrations let you tie workspaces directly to Git repositories for version-aware deployments.
  3. Scale dynamically: Leverage modular design to add new components without disrupting existing workflows. Whether expanding teams or introducing new AI modules, the server adapts fluidly.
  4. Monitor and maintain: Access detailed error logs and real-time status checks to troubleshoot issues before they impact production systems.

Terrakube MCP Server Features

Key Features of Terrakube MCP Server: Enterprise AI Deployment & Scaling?

What truly sets this tool apart is its type-safe architecture, built with TypeScript to catch errors before they reach runtime. The robust API integration isn't just a checkbox feature—it's designed for enterprise-grade reliability, supporting everything from microservices to multi-cloud deployments. Perhaps the most underrated feature is the sensitive variable handling, which encrypts credentials and API keys automatically while maintaining audit trails for compliance.

Use cases of Terrakube MCP Server: Enterprise AI Deployment & Scaling?

  • AI Model Orchestration: Deploy TensorFlow/Keras pipelines across distributed teams while maintaining version consistency through VCS-linked workspaces.
  • Compliance-Driven DevOps: Enforce strict variable policies for financial or healthcare AI models requiring HIPAA/GDPR compliance.
  • Multi-tenant Environments: Isolate R&D experiments from production deployments using organization-level permissions and workspace quotas.
  • CI/CD Integration: Automatically trigger retraining workflows when new data modules are published to the registry.

Terrakube MCP Server FAQ

FAQ from Terrakube MCP Server: Enterprise AI Deployment & Scaling?

  • Q: Does this require specialized infrastructure?
    A: No—works natively with Kubernetes clusters, cloud providers, or even local dev machines through flexible configuration profiles.
  • Q: How are conflicts resolved in multi-user environments?
    A: Built-in optimistic concurrency control ensures changes are atomic, with detailed roll-back capabilities for critical variables.
  • Q: Can I extend the server with custom modules?
    A: Absolutely—modular design allows adding new operation types without forking the core repository.
  • Q: What happens if API credentials expire?
    A: The server automatically refreshes tokens using OAuth2 flows, with configurable retry policies to minimize downtime.

Content

Terrakube MCP Server

A Model Context Protocol (MCP) server for Terrakube operations, enabling workspace management, variable handling, module operations, and organization management.

Features

  • Comprehensive API Integration : Full integration with Terrakube's API for seamless operations
  • Type Safety : Built with TypeScript for enhanced type safety and developer experience
  • Error Handling : Robust error handling with clear error messages
  • Environment Configuration : Flexible configuration through environment variables
  • Modular Design : Organized code structure for easy maintenance and extension

Tools

Workspaces

createWorkspace

Create a new workspace in Terrakube.

  • Inputs :
    • name (string): Name of the workspace
    • organization (string): Organization name
    • description (optional string): Workspace description
    • vcsProviderId (optional string): VCS provider ID
    • vcsRepository (optional string): VCS repository name
    • vcsBranch (optional string): VCS branch name
  • Returns : Created workspace details

updateWorkspace

Update an existing workspace.

  • Inputs :
    • name (string): Name of the workspace
    • organization (string): Organization name
    • description (optional string): New workspace description
    • vcsProviderId (optional string): New VCS provider ID
    • vcsRepository (optional string): New VCS repository name
    • vcsBranch (optional string): New VCS branch name
  • Returns : Updated workspace details

deleteWorkspace

Delete a workspace.

  • Inputs :
    • name (string): Name of the workspace
    • organization (string): Organization name
  • Returns : Success status

getWorkspace

Get details of a specific workspace.

  • Inputs :
    • name (string): Name of the workspace
    • organization (string): Organization name
  • Returns : Workspace details

listWorkspaces

List all workspaces in an organization.

  • Inputs :
    • organization (string): Organization name
  • Returns : Array of workspace details

Variables

createVariable

Create a new variable in a workspace.

  • Inputs :
    • name (string): Name of the variable
    • organization (string): Organization name
    • workspace (string): Workspace name
    • value (string): Variable value
    • description (optional string): Variable description
    • category (optional string): Variable category
    • hcl (optional boolean): Whether the variable is HCL
    • sensitive (optional boolean): Whether the variable is sensitive
  • Returns : Created variable details

updateVariable

Update an existing variable.

  • Inputs :
    • name (string): Name of the variable
    • organization (string): Organization name
    • workspace (string): Workspace name
    • value (string): New variable value
    • description (optional string): New variable description
    • category (optional string): New variable category
    • hcl (optional boolean): Whether the variable is HCL
    • sensitive (optional boolean): Whether the variable is sensitive
  • Returns : Updated variable details

deleteVariable

Delete a variable.

  • Inputs :
    • name (string): Name of the variable
    • organization (string): Organization name
    • workspace (string): Workspace name
  • Returns : Success status

getVariable

Get details of a specific variable.

  • Inputs :
    • name (string): Name of the variable
    • organization (string): Organization name
    • workspace (string): Workspace name
  • Returns : Variable details

listVariables

List all variables in a workspace.

  • Inputs :
    • organization (string): Organization name
    • workspace (string): Workspace name
  • Returns : Array of variable details

Modules

createModule

Create a new module.

  • Inputs :
    • name (string): Name of the module
    • organization (string): Organization name
    • provider (string): Module provider
    • description (optional string): Module description
  • Returns : Created module details

updateModule

Update an existing module.

  • Inputs :
    • name (string): Name of the module
    • organization (string): Organization name
    • provider (string): Module provider
    • description (optional string): New module description
  • Returns : Updated module details

deleteModule

Delete a module.

  • Inputs :
    • name (string): Name of the module
    • organization (string): Organization name
    • provider (string): Module provider
  • Returns : Success status

getModule

Get details of a specific module.

  • Inputs :
    • name (string): Name of the module
    • organization (string): Organization name
    • provider (string): Module provider
  • Returns : Module details

listModules

List all modules in an organization.

  • Inputs :
    • organization (string): Organization name
  • Returns : Array of module details

Organizations

createOrganization

Create a new organization.

  • Inputs :
    • name (string): Name of the organization
    • description (optional string): Organization description
  • Returns : Created organization details

updateOrganization

Update an existing organization.

  • Inputs :
    • name (string): Name of the organization
    • description (optional string): New organization description
  • Returns : Updated organization details

deleteOrganization

Delete an organization.

  • Inputs :
    • name (string): Name of the organization
  • Returns : Success status

getOrganization

Get details of a specific organization.

  • Inputs :
    • name (string): Name of the organization
  • Returns : Organization details

listOrganizations

List all organizations.

  • Returns : Array of organization details

Setup

Environment Variables

Create a .env file in the root directory with the following variables:

TERRAKUBE_API_URL=<your-terrakube-api-url>
TERRAKUBE_PAT_TOKEN=<your-personal-access-token>
TERRAKUBE_ORGANIZATION=<your-organization-name>

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/terrakube-mcp-server.git

cd terrakube-mcp-server
  1. Install dependencies:

    npm install

  2. Build the project:

    npm run build

Usage with Claude Desktop

To use this with Claude Desktop, add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "terrakube": {
      "command": "npx",
      "args": [
        "-y",
        "@terrakube/mcp-server-terrakube"
      ],
      "env": {
        "TERRAKUBE_API_URL": "<YOUR_API_URL>",
        "TERRAKUBE_PAT_TOKEN": "<YOUR_PAT_TOKEN>",
        "TERRAKUBE_ORGANIZATION": "<YOUR_ORGANIZATION>"
      }
    }
  }
}

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