mcp-server-terraform

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

Run plans, apply changes, inspect state, and diagnose failures — all from a Claude conversation.

What it does

Instead of switching to a terminal to run terraform plan, you can ask Claude:

"Plan the changes in /infra/staging and explain what will change"

"Apply it — but only if no resources will be destroyed"

"Show me all the outputs from the prod workspace"

"Something broke after the last apply — diagnose it"

The server translates these into real terraform CLI commands on your machine, with a built-in safety confirmation flow before any destructive operation runs.

Tools

Tool	Description	Destructive
tf_init	Initialize a Terraform working directory	No
tf_validate	Validate configuration syntax	No
tf_plan	Run a plan and return the diff + risk/cost summary	No
tf_apply	Apply changes (requires confirmed: true)	Yes
tf_destroy	Destroy infrastructure (requires confirmed: true)	Yes
tf_output	Read output values from state	No
tf_state	List, show, move, or remove state entries	Partial
tf_workspace	List, show, select, or create workspaces	No
tf_preflight	Check provider CLI authentication before running	No
tf_drift	Detect resources changed outside Terraform	No
tf_resource	Import, taint, untaint, or refresh resources	Yes

Plan risk & cost summary

Every plan (and every apply preview) is analyzed via terraform show -json and annotated with a structured summary — destroyed resources are called out, and always-on resources that commonly cause bill shock are flagged with rough monthly costs:

── Plan Summary ──
+ 3 create, ~ 1 update, - 0 destroy, ± 0 replace

💸 EXPENSIVE — always-on resources being created:
   ⚠ aws_nat_gateway.main (~$32/month + data processing if left running)
   Remember to tf_destroy when you're done experimenting.

Cost-flagged resource types include NAT gateways, load balancers, EKS/AKS/GKE control planes, RDS/Cloud SQL instances, ElastiCache, Redshift, MSK, and Azure Firewall (~$900/month!).

Drift detection

tf_drift runs a refresh-only plan and reports resources that were changed outside Terraform (e.g. manually in the cloud console), with the changed attribute names and remediation options.

Audit logging

Set AUDIT_LOG_PATH to a file path and every tool call is appended as a JSON line with timestamp, tool name, outcome, and duration. Variable values are always redacted (db_password=<redacted>) — only names are logged.

Confirmation flow

tf_apply and tf_destroy use a two-step safety flow:

1. First call (no confirmed) → runs terraform plan, shows the diff, does nothing else
2. Second call (confirmed: true) → actually applies or destroys

Claude is instructed to never pass confirmed: true without first presenting the plan to you.

Prompt

The server exposes a /tf-diagnose prompt that guides Claude through a systematic 5-step diagnosis of plan or apply failures.

Prerequisites

• Node.js 18 or later
• Terraform CLI on your PATH
• Claude Desktop (or any MCP-compatible client)

Installation

No install needed — run it straight from npm:

npx @rajsir/mcp-server-terraform

Or, for development, from source:

git clone https://github.com/RajeevSirohi/mcp-server-terraform.git
cd mcp-server-terraform
npm install
npm run build

Claude Desktop setup

Add to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "terraform": {
      "command": "npx",
      "args": ["-y", "@rajsir/mcp-server-terraform"]
    }
  }
}

(If running from source instead, use "command": "node" with "args": ["/absolute/path/to/dist/index.js"].)

Restart Claude Desktop. You should see a hammer icon indicating tools are available.

Usage

Once connected, just talk to Claude about your Terraform workspaces in plain language. Some examples:

First time in a new workspace

"Initialize the terraform config in C:\infra\staging and check if I'm logged into the right cloud accounts"

Claude runs tf_init, then tf_preflight — if you're not authenticated it tells you exactly which command to run (az login, aws configure, ...).

The everyday plan → review → apply loop

"Plan the changes in C:\infra\staging and explain what will change"

You get the plan diff plus a summary: how many resources created/updated/destroyed, anything destructive called out explicitly, and cost warnings for expensive always-on resources.

"Looks good, apply it"

Claude shows the plan preview one more time and asks for your confirmation — nothing is applied until you say yes. This two-step gate is built into the server itself, not just the prompt, so Claude cannot skip it.

Checking on your infrastructure

"Did anyone change anything outside terraform in the prod workspace?"

tf_drift compares state against reality and reports what was modified in the console, with options to accept or revert.

"Show me all the outputs" · "List everything in state" · "What workspaces exist?"

Learning / experimenting (e.g. cert prep)

"Apply the VPC lab in C:\labs\vpc, and when I say 'done' destroy everything"

The cost flags are your friend here — if a lab creates a NAT gateway or EKS cluster, the plan summary warns you what it costs per month if forgotten:

💸 EXPENSIVE — always-on resources being created:
   ⚠ aws_nat_gateway.main (~$32/month + data processing if left running)
   Remember to tf_destroy when you're done experimenting.

Fixing things

"terraform plan is failing in C:\infra\staging — diagnose it"

The /tf-diagnose prompt walks Claude through validate → providers → plan → state → outputs systematically. There's also /tf-login for step-by-step authentication setup per provider.

"Import the S3 bucket my-legacy-bucket into state as aws_s3_bucket.legacy"

"Taint the web server so it gets recreated on the next apply"

Recommended setup for shared or cautious environments

Run with ALLOW_ONLY_NON_DESTRUCTIVE_TOOLS=true (see Safety modes below) so apply/destroy are unavailable entirely, and set AUDIT_LOG_PATH so every operation is logged.

Safety modes

Control which tools are available via environment variables:

Variable	Effect
ALLOW_ONLY_READONLY_TOOLS=true	Only tf_validate, tf_plan, tf_output
ALLOW_ONLY_NON_DESTRUCTIVE_TOOLS=true	Blocks tf_apply, tf_destroy, tf_state mv/rm
ALLOWED_TOOLS=tf_plan,tf_output	Explicit comma-separated allowlist

Example — read-only mode:

{
  "mcpServers": {
    "terraform": {
      "command": "node",
      "args": ["/path/to/dist/index.js"],
      "env": {
        "ALLOW_ONLY_NON_DESTRUCTIVE_TOOLS": "true"
      }
    }
  }
}

Development

npm run dev       # watch mode (recompiles on save)
npm test          # run tests
npm run build     # production build

Project structure

src/
  index.ts                  # MCP server entry point, tool registration
  config/                   # (reserved for future config/telemetry)
  models/
    common-parameters.ts    # Shared Zod schemas
  security/
    tf-flags.ts             # Dangerous flag blocking
  tools/
    tf-init.ts
    tf-validate.ts
    tf-plan.ts
    tf-apply.ts             # Two-step confirmation flow
    tf-destroy.ts           # Two-step confirmation flow
    tf-output.ts
    tf-state.ts
    tf-workspace.ts
  utils/
    terraform-runner.ts     # Core exec wrapper, workspace switching
  prompts/
    index.ts                # /tf-diagnose prompt
tests/
  tf-flags.test.ts
  tf-apply.test.ts

Adding a new tool

1. Create src/tools/tf-yourcommand.ts — export a *Schema const and an async handler function
2. Import both in src/index.ts
3. Add the schema to readonlyTools or destructiveTools array
4. Add a case in the CallToolRequestSchema handler switch

Roadmap

• Terraform Cloud / Enterprise API support (Phase 2)
• OpenTelemetry tracing
• SSE / streamable HTTP transport for remote deployments
• Docker image on GitHub Container Registry
• Plan risk & cost analysis
• Drift detection
• Import / taint / untaint / refresh
• Audit logging
• CI (build, test matrix, e2e against real terraform)

Contributing

See CONTRIBUTING.md.

License

MIT — see LICENSE.