Have you ever wished your AI assistant could instantly surface the latest Terraform resources, modules, and syntax – without sending you off to dig through documentation? If you’re using GitHub Copilot for Infrastructure as Code, there’s a new way to make your workflow smarter and more up-to-date: the Terraform MCP Server.
In this blog post, we’ll discuss why you might want to use the Terraform MCP Server with GitHub Copilot and provide a step-by-step guide to setting it up for your Infrastructure as Code projects.
You are currently using GitHub Copilot for Terraform
Let’s set the scene. You’re in VS Code, building out cloud infrastructure with Terraform. GitHub Copilot sits beside you, suggesting resource blocks, variables, and even whole modules as you type. It’s like having a pair programmer who’s read every public repo on the internet.
Here’s what GitHub Copilot already brings to the table for Terraform users:
- Real-time code suggestions tailored to your context and file structure.
- Autocomplete for resource blocks – no more memorising every argument.
- Comment-driven code generation: Write a comment like “create an Azure storage account,” and GitHub Copilot drafts the code for you.
- Syntax error detection and inline documentation to help you avoid common pitfalls.
But there’s a catch: GitHub Copilot’s knowledge is only as current as the models last training run . That means it might miss out on the latest provider features, new modules, or breaking changes introduced after its data cutoff.
Enter Terraform MCP Server: Always up-to-date
This is where the Terraform MCP (Model Context Protocol) Server changes the game. When you connect GitHub Copilot to the MCP server, you unlock a direct line to the live Terraform Registry. Instead of relying on static, sometimes outdated AI knowledge, GitHub Copilot can now:
- Surface the newest providers, modules, and features the moment they’re released.
- Answer natural language questions with validated, real-time data—like “What’s the latest way to configure an Azure Key Vault?”
- Reduce manual research and context-switching, letting you stay in your editor and in the flow
| Scenario | GitHub Copilot Alone | GitHub Copilot & Terraform MCP Server |
| Recognises new provider features | Sometimes (If within AI model training data) | Yes, always latest |
| Suggests up-to-date syntax | Not guaranteed | Yes, direct from Terraform registry |
| Reduces manual searching | Partially | Dramatically |
| Keeps pace with Terraform updates | Can lag behind | Instantly up to date |
Imagine asking GitHub Copilot for an example using a brand-new resource type – like azurerm_management_group_policy_set_definition– just hours after it’s been added to the Terraform Registry. With the MCP server, GitHub Copilot can pull in the latest documentation and usage patterns, so you’re never left guessing or copy-pasting from outdated data
What can the Terraform MCP Server do for you?
Think of the MCP Server as a universal remote for your Terraform workflow:
| Feature | What It Means for You |
| Provider Discovery | Instantly explore available providers and their docs |
| Module Search & Analysis | Find, compare, and analyse modules with real usage data |
| Docker Support | Deploy the server anywhere Docker runs |
| AI-Driven Toolsets | Use tools like resolveProviderDocID and searchModules to fetch exactly what you need, when you need it |
Current available Tools from Terraform MCP Server:
| Tool | Description |
| resolveProviderDocID | Lists available docs for any Terraform provider – resources, data sources, functions, guides. |
| getProviderDocs | Pulls raw markdown docs for any resource/data source in a provider. Great for AI tools or custom doc search. |
| searchModules | Search for modules by keyword, verification status etc |
| moduleDetails | Dig deep into a module’s usage—inputs, outputs, submodules, examples. Perfect for reviewing before adoption. |
Why This Matters for You
- Faster onboarding: New team members get accurate, up-to-date help right in their editor.
- Fewer mistakes: You avoid deprecated syntax or missing arguments that could break deployments.
- Future-proof workflows: As Terraform evolves, your AI assistant evolves with it—no retraining required.
- AI that truly “knows” Terraform: The MCP server bridges the gap between static AI models and the ever-changing world of IaC
Getting Started: Setup in Minutes
Worried it’ll take ages to set up? Don’t be. If you have Docker installed and running, you’re already halfway there
Prerequisites
- Docker: Make sure Docker is installed and running on your machine.
Quick Setup
1. Create a config file: In your project workspace, add a file called .vscode/mcp.json with the following content:
{
"servers": {
"terraform": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"hashicorp/terraform-mcp-server"
]
}
}
}
2. Share the config: This setup means anyone on your team can use the same configuration—no more “works for me, not for you” drama.
3. Start using the tools: With the server running, you can now with GitHub Agent mode:
- Query provider docs with
resolveProviderDocID - Fetch full documentation using
getProviderDocs - Search for modules via
searchModules - Jump into module details with
moduleDetails
Example usage
Just last week, HashiCorp rolled out a brand-new resource: azurerm_management_group_policy_set_definition in version 4.35.0 of the terraform-provider-azurerm. I wanted to try it out out, but when I asked GitHub Copilot for help, it told me this resource didn’t exist yet.
But here’s where the MCP server made all the difference. Since it taps directly into the live Terraform Registry, I was able to access the documentation and examples for this new resource instantly. No more waiting for AI models to catch up—I rolled out the new resource in my project without a hitch.
It’s moments like these that really show the power of having up-to-the-minute knowledge at your fingertips.
Wrapping up
Setting up the Terraform MCP Server isn’t just about keeping up with the latest tech – it’s about making your day-to-day work smoother, faster, and a little less frustrating.
So, what do you think? Have you tried the MCP Server yet? Did it change the way you work with Terraform?