Skip to main content
Glama

tf-dialect

by UtpalJayNadiger
GitHub
TypeScript
MIT License
OverviewInspectNewEndpointsSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

tf-dialect

tf-dialect is an MCP (Model Context Protocol) server that exposes your organization's Terraform style guide to AI coding agents, ensuring they generate context-aware, organization-specific Infrastructure as Code instead of generic HCL.

Configure once, use with any MCP-capable coding agent (Claude Desktop, Cline, etc.).

Quick Start

# Clone the repository git clone https://github.com/utpaljaynadiger/tf-dialect.git cd tf-dialect # Install dependencies npm install # Build the project npm run build # Create your style configuration cp terraform-style.example.yaml terraform-style.yaml # Edit terraform-style.yaml with your organization's standards # Then configure in your MCP client (see "Running the Server" section)

Features

  • 📚 Style Guide Management: Define your Terraform conventions in a single YAML file

  • 🔍 Validation: Check Terraform snippets against your organization's rules

  • 📝 Code Examples: Provide reusable snippets for common patterns

  • 🛡️ Security Defaults: Enforce security best practices automatically

  • 🏗️ Code Generation: Generate compliant Terraform resources

  • 🤖 AI-Native: Works seamlessly with MCP-capable coding agents

Installation

npm install npm run build

Configuration

  1. Copy the example config:

cp terraform-style.example.yaml terraform-style.yaml

  1. Edit terraform-style.yaml to match your organization's standards:

modules: pattern: "root + shared-modules" shared_module_path: "modules/" prefer_shared_modules: true naming: resource_format: "<project>-<env>-<component>-<extra?>" variable_case: "snake_case" output_case: "snake_case" tagging: required_tags: - "environment" - "owner" - "cost_center" defaults: environment: "${var.environment}" owner: "infra-team" security_defaults: s3_bucket: block_public_acls: true versioning: true encryption: "aws:kms" rds: storage_encrypted: true backup_retention_period: 7 examples: s3_private_bucket: | module "logs_bucket" { source = "../modules/s3-bucket" name = "${local.project}-${var.environment}-logs" tags = local.default_tags }

Running the Server

Standalone

npm run mcp

With Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "tf-dialect": { "command": "node", "args": ["/absolute/path/to/tf-dialect/dist/index.js"], "env": { "TERRAFORM_STYLE_PATH": "/absolute/path/to/your/terraform-style.yaml" } } } }

Or if terraform-style.yaml is in the same directory as the server:

{ "mcpServers": { "tf-dialect": { "command": "node", "args": ["/absolute/path/to/tf-dialect/dist/index.js"] } } }

With Cline VSCode Extension

Add to your MCP settings:

{ "mcpServers": { "tf-dialect": { "command": "node", "args": ["/absolute/path/to/tf-dialect/dist/index.js"] } } }

MCP Tools

The server exposes four tools that AI agents can use:

1. get_style_guide

Get the complete Terraform style guide configuration.

Input: None

Output:

{ "modules": { ... }, "naming": { ... }, "tagging": { ... }, "providers": { ... }, "security_defaults": { ... }, "examples": { ... } }

Example agent prompt:

"Show me the Terraform style guide for this project"

2. list_examples

List code examples, optionally filtered by resource type or search term.

Input:

{ "resourceType": "s3_bucket", // optional "search": "postgres" // optional }

Output:

{ "examples": [ { "name": "s3_private_bucket", "code": "module \"logs_bucket\" { ... }" } ] }

Example agent prompts:

"Show me examples of S3 buckets" "List all RDS examples"

3. validate_snippet

Validate Terraform code against the style guide.

Input:

{ "code": "resource \"aws_s3_bucket\" \"example\" { ... }", "filePath": "main.tf" // optional }

Output:

{ "valid": false, "violations": [ { "ruleId": "required_tag_missing", "severity": "error", "message": "Missing required tags: environment, owner", "line": 5, "suggestion": "Add the following tags: environment = \"...\", owner = \"...\"" } ] }

Example agent prompts:

"Validate this Terraform code against our style guide" "Check if this S3 bucket configuration is compliant"

4. generate_resource

Generate a Terraform resource following organization standards.

Input:

{ "resourceType": "aws_s3_bucket", "env": "prod", "service": "analytics", "purpose": "logs", // optional "extraTags": { // optional "team": "data" } }

Output:

{ "code": "resource \"aws_s3_bucket\" \"this\" { ... }" }

Supported resource types:

  • aws_s3_bucket

  • aws_db_instance

  • Others (generates generic stub with TODOs)

Example agent prompts:

"Generate an S3 bucket for prod analytics logs" "Create an RDS instance for the staging API database"

Validation Rules

tf-dialect enforces the following rules:

Required Tags

Ensures all resources include required tags defined in your config.

Forbidden Patterns

Blocks dangerous patterns like:

  • 0.0.0.0/0 in security groups

  • Hardcoded credentials

  • Custom regex patterns you define

Security Defaults

Enforces security best practices:

S3 Buckets:

  • Block public access

  • Enable versioning

  • Enable encryption (KMS or AES256)

RDS Instances:

  • Enable storage encryption

  • Set backup retention period

  • Other configurable defaults

Naming Conventions

Validates resource names follow your format:

  • <project>-<env>-<component>-<extra?>

  • Checks component count and structure

Development

# Install dependencies npm install # Build npm run build # Watch mode npm run dev

Example Workflow

  1. Agent asks about style:

    • Agent calls get_style_guide

    • Learns your organization's conventions

  2. Agent needs an example:

    • Agent calls list_examples with resourceType: "rds"

    • Gets working RDS configuration examples

  3. Agent generates code:

    • Agent calls generate_resource or writes code

    • Then calls validate_snippet to check compliance

  4. Agent fixes violations:

    • Reads violation suggestions

    • Updates code to be compliant

Use Cases

  • Onboarding: New team members' AI assistants learn your standards instantly

  • Consistency: All Terraform code follows the same patterns across teams

  • Security: Enforce security defaults automatically in generated code

  • Productivity: AI generates compliant code on first try, not generic HCL

License

MIT

Contributing

Contributions welcome! This is an OSS-friendly project designed for IaC power users.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Exposes your organization's Terraform style guide to AI coding agents, enabling them to generate, validate, and ensure Infrastructure as Code follows your specific conventions, naming standards, security defaults, and best practices.

  1. Quick Start
    1. Features
      1. Installation
        1. Configuration
          1. Running the Server
            1. Standalone
            2. With Claude Desktop
            3. With Cline VSCode Extension
          2. MCP Tools
            1. 1. get_style_guide
            2. 2. list_examples
            3. 3. validate_snippet
            4. 4. generate_resource
          3. Validation Rules
            1. Required Tags
            2. Forbidden Patterns
            3. Security Defaults
            4. Naming Conventions
          4. Development
            1. Example Workflow
              1. Use Cases
                1. License
                  1. Contributing

                    New MCP Servers

                    MCP directory API

                    We provide all the information about MCP servers via our MCP API.

                    curl -X GET 'https://glama.ai/api/mcp/v1/servers/UtpalJayNadiger/tf-dialect'

                    If you have feedback or need assistance with the MCP directory API, please join our Discord server