HashiCorp released the Terraform MCP Server (https://github.com/hashicorp/..., an open-source implementation of the Model Context Protocol. It aims to enhance how large language models interact with infrastructure as code by exposing Terraform Registry data in a structured format. This enables AI systems to ground their suggestions in current configuration patterns, allowing tools like Claude, Co-Pilot, and ChatGPT to generate more accurate code.

The Model Context Protocol (MCP) is a standard for AI to retrieve structured data from external systems in real time via JSON-over-GRPC. In the Terraform MCP Server implementation, it serves as a bridge between AI systems and the Terraform Registry, exposing data about modules, providers, and resources.

This setup allows AI models to get up-to-date configuration details by issuing standardized queries. It helps AI-assisted tools align with Terraform standards and mitigates issues from relying on outdated knowledge. Although in early development, it has been integrated with GitHub Copilot at Microsoft Build 2025.

Independent projects like terraform-docs-mcp and tfmcp are also experimenting with the MCP protocol. terraform-docs-mcp implements an MCP server in Node.js to surface module metadata, while tfmcp explores a CLI-driven approach with LLMs. These community efforts show growing interest in the MCP ecosystem.

Terraform MCP Server is an example of a broader pattern in AI-assisted tooling to unify developer workflows. The adoption of such protocols indicates a shift from product-specific AI integrations to interoperable interfaces for a diverse ecosystem.