This project provides a Model Context Protocol (MCP) server designed to integrate with the Terraform Registry, enabling advanced automation and data analysis for Infrastructure as Code (IaC). It targets developers and researchers working with Terraform, offering capabilities for provider and module discovery, documentation extraction, and detailed analysis of IaC components.

How It Works

The server acts as an intermediary, querying Terraform Registry APIs to retrieve and process information about providers and modules. It utilizes a toolset for specific actions: resolveProviderDocID and getProviderDocs for provider documentation, and searchModules and moduleDetails for module information. This approach allows for structured, programmatic access to registry data, facilitating automated workflows and deeper understanding of IaC assets.

Quick Start & Requirements

• Installation: Run via Docker: docker run -i --rm hashicorp/terraform-mcp-server. Configuration examples are provided for VS Code and Claude Desktop.
• Prerequisites: Docker is required for containerized execution. Building from source requires Go.
• Resources: No specific hardware or dataset requirements are mentioned.
• Links: VS Code agent mode documentation, Claude Desktop user documentation.

Highlighted Details

• Enables automation of Terraform provider and module discovery.
• Facilitates extraction and analysis of data from the Terraform Registry.
• Provides detailed information on provider resources, data sources, and module configurations.
• Supports fetching raw documentation in Markdown format.

Maintenance & Community

• Support is available via GitHub Issues for bug reports/feature requests and GitHub Discussions for general questions.
• Security issues should be reported to security@hashicorp.com.
• Contribution guidelines are provided, encouraging forks and pull requests.

Licensing & Compatibility

• Licensed under the Mozilla Public License 2.0 (MPL-2.0).
• No explicit restrictions on commercial use or closed-source linking are mentioned, consistent with MPL-2.0.

Limitations & Caveats

Outputs and recommendations are dynamically generated and require thorough review by users to ensure alignment with security, cost-efficiency, and compliance requirements.