"A platform for managing infrastructure as code with Terraform Cloud" matching MCP servers:

Enables Terraform HCL plan generation, drift detection, and strict human confirmation approval barriers for cloud infrastructure management.

A Model Context Protocol (MCP) server that integrates Claude with the Terraform Cloud API, allowing Claude to manage your Terraform infrastructure through natural conversation.

A read-only MCP server for inspecting and diagnosing HCP Terraform workspaces, runs, and plan summaries from ChatGPT and other MCP clients. It provides tools to list workspaces, inspect runs, and summarize plans without exposing sensitive data.

MCP server for managing Terraform infrastructure as code, enabling initialization, planning, applying, destroying, validating, and showing state of Terraform configurations.

An advisory and generative copilot for Terraform that reviews existing scripts and generates compliant infrastructure using organization-specific rules.

A Model Context Protocol (MCP) server that lets Claude manage Terraform infrastructure through natural language.

Enables AI assistants to interact with Terraform Cloud workspaces and runs, including checking run status, listing workspaces, and retrieving detailed information about workspaces and runs.

Enables management of Terraform infrastructure as code through 25 comprehensive tools for operations including plan/apply/destroy, state management, workspace management, and configuration file editing.

The Terraform MCP Server is a Model Context Protocol (MCP) server that integrates seamlessly with Terraform Registry and HCP Terraform APIs, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development.

Enables state-aware Terraform/OpenTofu plan evaluation against live AWS topology and CloudWatch telemetry, detecting reliability blast radius and FinOps waste before merge.

An MCP server that exposes Terraform CLI operations as tools for Claude and other MCP-compatible AI assistants, enabling AI-driven infrastructure-as-code workflows including planning, applying, and validating Terraform configurations.

Enables generating, validating, planning, and applying Terraform configurations to manage infrastructure through natural language.

A FastMCP-based server that provides a natural language interface to Terraform operations, enabling infrastructure management via simple English commands.

Enables AI agents to manage Terraform modules by searching, inspecting, and scaffolding configurations from private GitHub repositories. Also provides backend S3 configuration for generated Terraform code.

Enables coding agents to answer from what is actually true in the current Terraform working directory: the provider schemas pinned in the lock file, saved plans, redacted state, and local or installed module interfaces. It also reports what a pinned provider upgrade would break, all read-only and without ever running init in the root module.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Connects AI models to the Terraform Registry via MCP, enabling provider lookups, resource usage examples, and module recommendations for streamlined Terraform workflows.

An MCP server and terminal UI that orchestrates Cloudflare, Namecheap, and Fleet from a single interface, enabling domain onboarding with automatic DNS migration and security hardening.

Exposes Terraform/Terragrunt tooling as Claude Code tools for generating variables.tf and mock outputs automatically from input files.

MCP server for auditing infrastructure-as-code attack paths, finding multi-hop chains from public internet to sensitive resources.

Enables AI assistants to search and retrieve information about Terraform providers and modules from the public Terraform registry, including detailed documentation, version information, and resource specifications.

A Model Context Protocol (MCP) server for producing better Terraform through CLI analysis (tflint, checkov, trivy, kics, infracost), best-practice guidance from terraform-best-practices.com, cloud provider recommendations (Azure, AWS, GCP), and Terraform Registry resource and module guidance.

Delegates expensive or long-running AI tasks to Kubernetes-hosted Claude agents running on OpenShift. Enables creating, managing, and monitoring remote agentic sessions for complex work like codebase analysis and security audits.

Enables AI assistants to manage multi-cloud resources (AWS, Azure, GCP) including resource operations, cost analysis, monitoring metrics, and security compliance checks through natural language commands.

Exposes eight MCP tools over streamable HTTP that let clients ingest alerts, list and diagnose incidents, analyze Terraform plan risk, propose remediations, and dry-run or execute policy-validated, approval-gated changes with automatic rollback.

Wraps multiple cloud CLIs (AWS, GCP, Azure, OCI) into a unified command interface and exposes them as MCP tools for AI assistants.

Reference MCP server for IOX Cloud — a deployment platform for MCP servers and AI agents. Deploys to 300+ edge locations via Cloudflare Workers for Platforms with per-tenant V8 isolation, built-in OAuth 2.1, and usage tracking. This repo demonstrates a minimal MCP server that introspects the IOX Cloud platform (tools: get_platform_info, list_templates, estimate_cost, check_slug_available).

Provides tools to execute Terraform commands, analyze plan outputs for resource changes, and generate detailed markdown reports. It supports custom environment variables and filtering patterns to streamline infrastructure analysis and management.

Enables Claude AI to automatically audit AWS cloud resource configurations, diagnose security vulnerabilities, and generate high-availability optimization reports.

A read-only MCP server that analyzes a supplied Terraform plan JSON file and returns resource identities, action/type/mode counts, and coverage warnings about proposed infrastructure changes. It never executes Terraform, applies changes, or connects to AWS, keeping analysis local and deterministic.