Turn English Feature Requests into Tested Terraform Plans with Cursor AI in VS Code (2026)

Why Turn English Requests into Terraform Plans?

Teams spend weeks writing HCL for new services. In practice, the bottleneck is not the code itself but the back-and-forth with docs and reviewers. Cursor AI can read a plain English description—"Add a private S3 bucket for logs with encryption and lifecycle policy"—and output a ready-to-apply Terraform module. The result is faster delivery, fewer syntax errors, and a single source of truth for both product and infrastructure teams.

In 2026, the combination of Cursor’s Agent mode and the new Terraform MCP server means the AI has live access to your state, provider schemas, and module registry. That eliminates the guesswork that older AI tools suffered from.

When you test the generated plan with Terratest, you get confidence that the code works before it ever hits production.

Prerequisites for the 2026 Workflow

Before you start, make sure you have the following installed:

• VS Code 1.92 or newer.
• Cursor AI extension version 2.4+ (supports MCP).
• Terraform CLI 1.7+ and the AWS provider 5.0+ (or your cloud equivalent).
• Node.js 20+ for Terratest scripts (written in TypeScript).
• An active OpenAI/Anthropic API key with access to Claude-4o (the cheapest high-quality model for code).

Optional but recommended: a local Terraform MCP server (see the GitHub repo terraform-smart-context-mcp-42) to give Cursor real-time insight into your existing state.

Step-by-Step: From English to Tested Plan

1. Set up the MCP server. Clone the repo, run npm install && npm run start, and add the following to .cursor/mcp.json in your repo root:

{
  "mcpServers": {
    "terraform": {
      "command": "npx",
      "args": ["tsx", "./src/index.ts"],
      "env": { "TERRAFORM_MCP_GATE": "read" }
    }
  }
}

This gives Cursor read-only access to your state, provider schemas, and module graph.

2. Open a new file in VS Code and start a Cursor chat. Use the command palette → "Cursor: New Chat". In the chat window, type your feature request, for example:

Add a private S3 bucket named app-logs in the us-east-1 region. Enable server-side encryption with KMS, add a lifecycle rule to delete objects after 90 days, and output the bucket ARN.

Make sure you include any naming conventions or tags your org uses. Cursor will ask follow-up questions if needed.

3. Let Cursor generate the module. The AI will respond with a full directory tree, HCL files, and a README.md. Review the output quickly; the AI usually formats correctly because it knows the HCL grammar.

4. Validate locally. Open the generated folder in a terminal and run:

terraform init
terraform validate
terraform plan -var-file=../environments/dev/terraform.tfvars

If terraform validate fails, copy the error back into the Cursor chat. The AI can fix syntax issues in under a minute.

5. Add automated tests. Cursor can also scaffold Terratest code. Ask it:

Write a Terratest in TypeScript that checks the bucket exists, has encryption enabled, and the lifecycle rule is set.

The AI will produce a test/bucket.test.ts file. Run it with npm test. If the test fails, paste the output back to Cursor for a quick fix.

6. Commit and open a PR. The generated code includes a conventional commit message and a PR template that highlights the original English request, the generated plan, and test results.

Comparison: Cursor AI vs VS Code Copilot for Terraform (2026)

The table shows why many DevOps teams prefer Cursor for large, multi-module changes, while Copilot still shines for quick, single-file edits.

Practical Takeaways – Who Should Use This?

Infrastructure engineers building new services. They can prototype an entire stack in minutes and move straight to testing.

Product managers with technical background. They can write feature specs in plain English and let Cursor produce the infra code, speeding up hand-off.

Small startups on a tight budget. The low token cost and automated testing reduce both time and cloud spend.

Large enterprises with strict compliance. By feeding the MCP server with policy-as-code (OPA, Sentinel), Cursor respects guardrails automatically.

Common Pitfalls and How to Avoid Them

Even with AI, mistakes happen. Here are three real-world issues we’ve seen in 2026 and fixes.

• Missing provider version constraints. Cursor may omit the exact provider version. After generation, run terraform providers lock and add the lock file to version control.
• Incorrect resource naming conventions. If your org prefixes all resources with prod-, add a rule in .cursor/rules.json that enforces the prefix. Cursor will then prepend it automatically.
• State drift after generation. Always run terraform plan against the live state before merging. If the plan shows unexpected changes, ask Cursor to reconcile the diff.

Future Outlook

By late 2026, Cursor plans to add “plan-preview” mode that streams the terraform plan output back into the chat, letting you approve line-by-line without leaving VS Code. Keep an eye on the release notes; the workflow described here will become even tighter.

Conclusion

Using Cursor AI inside VS Code to turn plain English feature requests into fully tested Terraform plans is no longer a novelty—it’s a practical, cost-effective workflow for 2026. The combination of MCP context, fast Claude-4o models, and built-in test scaffolding gives teams speed and confidence. Whether you’re a solo developer or part of a regulated enterprise, the steps above let you adopt the process today.

"In practice, Cursor reduced our Terraform scaffolding time from days to under an hour while keeping compliance checks intact," says Maya Patel, Senior DevOps Engineer at CloudScale (2026).