"terraform" matching MCP servers:

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

An MCP server that wraps terraform-ls to provide Terraform language intelligence as MCP tools, enabling AI assistants to understand and navigate Terraform codebases.

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 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 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.

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

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

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.

Enables AI coding agents to plan, policy-check, and cost-estimate Terraform changes while requiring human approval through Slack or CLI before any terraform apply can execute.

Enables Claude to discover and understand infrastructure-as-code drift by listing projects with drift, analyzing Terraform plan outputs, and providing remediation information.

An MCP server for running the Infracost CLI to estimate, compare, and publish infrastructure cost changes from Terraform and other IaC projects.

Enables MCP-capable clients to discover live Oracle Cloud Infrastructure resources, map their dependencies, and generate reproducible Terraform configurations, all while enforcing security modes, compartment/region allowlists, and secret redaction.

Enables AI assistants to visualize cloud architecture diagrams, generate and import Terraform HCL, and manage infrastructure resources directly from chat through the CloudForge platform.

Enables AI agents to manage Google Cloud Platform resources including projects, IAM, service accounts, and APIs directly through GCP REST APIs without requiring gcloud CLI or Terraform.

Enables Claude to interact with Infracost for Terraform cost estimation and cloud governance. Supports generating cost breakdowns, comparing configurations, creating tagging policies, setting up cost guardrails, and posting cost comments to PRs.

An MCP server that enables Claude to manage infrastructure across Kubernetes, Docker, Prometheus, and Terraform through natural language. It provides over 42 specialized tools with a safety-first design, including risk-based command classification and audit logging.

Enables AI agents and LLM workflows to run common CLI tools like kubectl, terraform, helm, git, and jq while receiving structured JSON with typed schemas instead of raw human-readable output.

Enables AI agents to execute read-only DevOps operations including kubectl, terraform, helm, docker, and AWS cost analysis through natural language.

An MCP server for Infrastructure as Code that enables AI assistants to manage cloud resources through Ansible and Terraform operations. It supports executing playbooks, managing AWS services, and running Terraform commands with optional LocalStack integration.

MCP server that evaluates Terraform plans, shell commands, and tool calls to assess recoverability and risk before execution, enabling safe AI agent actions.