An MCP server that provides tools for interacting with the Terraform Registry API

An IaC component memory enhancement server that provides persistent storage for Claude AI, focusing on version tracking and relationship mapping of Terraform and Ansible resources

The Terrakube MCP Server is a Model Context Protocol server for managing Terrakube operations, providing comprehensive API integration for workspace, variable, module, and organization management.

An MCP server built on Deno and TypeScript, providing context information related to Terragrunt documentation and GitHub issues, aiming to improve IDE support and AI assistance accuracy when writing IaC configurations.

An MCP server used to obtain the latest stable versions of dependent packages in multiple ecosystems (such as Python, NPM, Go, GitHub Actions, etc.) and the latest versions of nearly a thousand development tools (such as Python, Node.js, kubectl, Terraform, etc.), helping AI programming assistants avoid using outdated dependency versions when generating code.

A Terraform module RAG engine that supports automatic import of multiple repositories, code analysis, vector storage, and semantic search, providing CLI, API, and MCP service interfaces.

An MCP server based on Deno and TypeScript that provides context information query functions for Terragrunt documentation and GitHub issues, aiming to improve the Terragrunt development experience and the accuracy of AI-assisted tools.

The Terrakube MCP Server is a model context protocol server developed based on TypeScript, designed specifically for the Terrakube platform, providing functions such as workspace management, variable handling, module operations, and organization management.

A Temporal-based workflow orchestration system for managing multi-workspace Terraform deployments, supporting dependency resolution, variable passing, and MCP server integration for AI-driven automation.

The Terraform Cloud MCP Server is an AI assistant integrated service for managing infrastructure through natural language conversations, supporting multiple MCP-compatible platforms.

Terry-Form MCP is a bridge connecting AI assistants and Terraform infrastructure management. It executes Terraform commands in a secure container environment and integrates the Language Server Protocol (LSP) to provide intelligent development capabilities.

This project demonstrates how to create an MCP server using Azure Functions and interact through the proxy mode of GitHub Copilot Chat. It includes configuration methods for local and remote MCP servers, as well as a guide for deploying Azure Functions via Terraform.

An MCP server that provides persistent storage for infrastructure-as-code (IaC) for Claude AI, focusing on version tracking and relationship mapping of Terraform and Ansible resources.

An infrastructure as code management server based on Terraform, providing full - lifecycle management functions such as initialization, planning, application, and destruction, and supporting local and Docker deployment.

The Terraform provider for F5 Distributed Cloud, supporting the management of F5 Distributed Cloud resources through Infrastructure as Code

Agent Polis is an impact preview system for AI agent actions, providing a function similar to 'Terraform plan'. It analyzes the impact of autonomous AI agents before they execute operations, displays a difference preview, and requires manual approval before execution, aiming to prevent AI agents from performing dangerous operations.

The Terraform MCP server developed by Binadox provides functions such as automatic verification, security checks, and cost estimation for Terraform configurations, and integrates the MCP protocol to work in collaboration with an AI assistant.

This project is an AWS Bedrock Guardrails management server that provides functions for creating, editing, deleting, and exporting guardrails (including all policy types), and supports exporting configurations as Terraform files.

An MCP server integrated with the HCP Terraform Cloud/Enterprise API, providing functions to search for private modules and obtain module details.