terraform-guardrail 0.2.8

MCP-backed Terraform assistant with ephemeral-values compliance.

Terraform Guardrail MCP

Terraform Guardrail MCP (Model Context Protocol) is a governance control plane for Infrastructure as Code. It runs outside Terraform and gives AI assistants and platform teams real provider context, policy intelligence, and auditable guardrails so every change is safer by default. The result is a cleaner state, fewer failures, and a shorter path from idea to production.

This product is built for teams shipping infrastructure at scale who need speed without sacrificing safety. Guardrail enforces non-negotiable platform invariants, allows composable product constraints, and produces human-readable reports that make decisions obvious and defensible.

Live app: https://terraform-guardrail.streamlit.app/

Design Principle

Non-negotiable safety floor, composable freedom above it. Guardrails live outside Terraform.

User Perspective (High-Level)

flowchart LR
    USER[Platform + Product Teams] --> CHANNELS[CLI • Streamlit • REST API • MCP]
    CHANNELS --> GUARDRAIL[Terraform Guardrail MCP]
    GUARDRAIL --> REPORTS[Readable Guidance + Evidence]
    GUARDRAIL --> TERRAFORM[Safer Terraform Applies]

Ways Developers Use Guardrail

flowchart LR
    DEV[Developer] --> CLI[CLI]
    DEV --> UI[Streamlit UI]
    DEV --> API[REST API]
    DEV --> MCP[MCP for AI Assistants]
    DEV --> CI[GitHub Action]
    DEV --> ADO[Azure DevOps]
    CLI --> GUARDRAIL
    UI --> GUARDRAIL
    API --> GUARDRAIL
    MCP --> GUARDRAIL
    CI --> GUARDRAIL
    ADO --> GUARDRAIL

Current Capabilities

• Multi-file scanning with summaries and CSV export
• Secret hygiene checks across .tf, .tfvars, and .tfstate
• Schema-aware validation with Terraform CLI integration
• Provider metadata lookup via Terraform Registry
• MCP tools for scan, metadata, and snippet generation
• Streamlit and web UI for instant reporting
• Dockerized REST API for CI/CD adoption
• Docker Compose dev stack (API + UI + policy registry, optional analytics)

Supported Providers

• AWS
• Azure
• GCP
• Kubernetes
• Helm
• OCI
• Vault
• Alicloud
• vSphere

Feature Matrix

Area	CLI	Web UI / Streamlit
Config scan (.tf, .tfvars, .hcl)	Yes	Yes
State leak scan (.tfstate)	Yes	Yes
Schema-aware validation	Yes	Yes
CSV export	No	Yes
Provider metadata	Yes	Yes
Snippet generation	Yes	No
Multi-file scan	Yes (directory)	Yes (upload up to 10)
Human-readable report	Yes	Yes

Architecture (High-Level)

flowchart TB
    subgraph Interfaces
        CLI([CLI])
        MCP([MCP Server])
        WEB([Web UI])
        API([REST API])
        STL([Streamlit App])
    end

    subgraph Core
        SCAN((Compliance Engine))
        GEN[[Snippet Generator]]
        POLICY{Policy Layering}
    end

    subgraph Integrations
        TF[/Terraform CLI/]
        REG[(Terraform Registry)]
    end

    CLI --> SCAN
    WEB --> SCAN
    API --> SCAN
    STL --> SCAN
    MCP --> SCAN
    MCP --> GEN
    SCAN --> POLICY --> TF
    GEN --> REG

    classDef interface fill:#e3f2fd,stroke:#1e88e5,stroke-width:1px,color:#0d47a1;
    classDef core fill:#e8f5e9,stroke:#2e7d32,stroke-width:1px,color:#1b5e20;
    classDef integration fill:#fff3e0,stroke:#ef6c00,stroke-width:1px,color:#e65100;

    class CLI,MCP,WEB,API,STL interface;
    class SCAN,GEN,POLICY core;
    class TF,REG integration;

Architecture (Detailed Flow)

flowchart TB
    INPUTS[Inputs: .tf, .tfvars, .tfstate] --> PARSE[Parse & Normalize]
    PARSE --> SCHEMA[Provider Schema + Metadata]
    SCHEMA --> RULES[Apply Guardrail Rules]
    RULES --> REPORT[Findings + Summary]
    REPORT --> OUTPUTS[CLI JSON • UI • MCP • REST]

Note: Mermaid diagrams render on GitHub and in the Wiki. PyPI will show the raw blocks.

Roadmap (Major Releases)

Legend: ✅ Delivered • 🚧 Under construction

Deliverable	v1.0 Foundation	v2.0 Enterprise	v3.0 Ecosystem	v4.0 Intelligent
Dockerized MCP + REST API	✅ Delivered (0.2.x)
CLI-first install	✅ Delivered (0.2.x)
Docker Compose local stack (API + UI + registry)	✅ Delivered (0.2.x)
GitHub Action pre-apply / PR checks	🚧 Planned
Azure DevOps / Pipeline extension	🚧 Planned
Policy layering model (base → env → app)	🚧 Planned
Policy metadata + rich failure messages		🚧 Planned
Drift-prevention rules before apply		🚧 Planned
Central guardrail registry		🚧 Planned
Policy versioning + audit trail		🚧 Planned
Contributor governance + public roadmap			🚧 Planned
Reference implementations across tools			🚧 Planned
Cross-provider invariant enforcement			🚧 Planned
Context-aware evaluation				🚧 Planned
Suggested fixes + recommendations				🚧 Planned

Comparison with Other Tools

Terraform Guardrail MCP takes a fundamentally different approach to IaC governance than traditional scanning or linting tools. Guardrail is delivered as a Model Context Protocol (MCP) server with a CLI and web UI. It runs outside Terraform, exposing provider metadata, scanning configs and state for sensitive values, and producing human-readable reports. Its rules engine focuses on secret hygiene and write-only arguments and lets platform teams publish non-negotiable guardrails while product teams compose contextual constraints.

By contrast, existing tools such as Checkov, TFLint and OPA/Conftest operate mainly as static code analyzers embedded in CI pipelines. They scan Terraform files or plans for misconfigurations but do not provide a centralized control plane or cross-provider context. The table below summarizes the key differences:

Category	Guardrail MCP	Checkov	TFLint	OPA/Conftest
Primary purpose	External IaC governance control plane	Static multi-IaC security scanner	Terraform linter	General policy engine (Rego)
IaC support	Terraform + multi-cloud providers (AWS, Azure, GCP, Kubernetes, Helm, OCI, Vault, vSphere, Alicloud)	Terraform, CloudFormation, Kubernetes, Helm, ARM, Serverless	Terraform (HCL)	Any domain via Rego policies
Policy model	Central guardrail registry; platform invariants + product constraints; versioned and auditable	Built-in rules (Python/Rego) + custom policies	Provider-specific rule plugins; experimental Rego plugin	Rego rules only
Enforcement stage	Pre-apply; prevents bad state and drift; uses provider schemas	Pre-apply scan of templates and plans	Pre-apply linting for errors and best-practice drifts	Pre-apply checks (via Conftest) – outcome depends on integration
Governance & audit	Org-level guardrail registry, ownership boundaries, audit trail	No policy lifecycle management	No policy registry	No governance features
Developer experience	CLI/Server/Web UI; human-readable reports & fix suggestions	CLI with JSON/SARIF/JUnit output and graph insights	CLI with JSON/SARIF/JUnit output; configurable warnings	CLI library; steep learning curve

Why Guardrail complements scanners

Checkov provides a vast policy library and graph-based resource analysis to catch misconfigurations early, and TFLint offers pluggable, provider-aware linting rules to detect invalid types, deprecated syntax and best-practice drifts. These tools remain valuable for static analysis of Terraform code. Guardrail MCP builds upon them by acting as a higher-order control plane: it uses provider metadata to validate schema usage, prevents secret leakage and drift before Terraform mutates state, and separates platform-owned safety floors from product-level constraints. In practice, teams often run TFLint or Checkov in their CI to catch coding errors while Guardrail serves as the last line of defense to enforce organizational guardrails and deliver contextual guidance.

Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -e "[dev]"

# CLI scan
terraform-guardrail scan examples

# snippet generation
terraform-guardrail generate aws aws_s3_bucket --name demo

# MCP server (stdio)
terraform-guardrail mcp

# Web UI
terraform-guardrail web

Install from PyPI

pip install terraform-guardrail

PyPI: https://pypi.org/project/terraform-guardrail/ (latest: 0.2.8)

CLI examples

# scan a directory
terraform-guardrail scan ./examples --format json

# scan state files too
terraform-guardrail scan ./examples --state ./examples/sample.tfstate

# enable schema-aware validation (requires terraform CLI + initialized workspace)
terraform-guardrail scan ./examples --schema

Web UI

Visit http://127.0.0.1:8000 and upload a Terraform file to view a compliance report.

Streamlit App

streamlit run streamlit_app.py

Live app: https://terraform-guardrail.streamlit.app/

Streamlit Cloud deployment

1. Push this repo to GitHub.
2. Create a new Streamlit Cloud app.
3. Set the main file path to streamlit_app.py.
4. Deploy (Streamlit will install from requirements.txt).

REST API (Docker)

Build and run the API server:

docker build -t terraform-guardrail .
docker run --rm -p 8080:8080 terraform-guardrail

API endpoints:

• GET /health
• GET /metrics
• POST /scan
• POST /provider-metadata
• POST /generate-snippet

Example request:

curl -X POST http://localhost:8080/scan \\
  -H "Content-Type: application/json" \\
  -d '{"path":"./examples","use_schema":false}'

Container Image

Pull the published container image (built on release tags):

docker pull ghcr.io/huzefaaa2/terraform-guardrail:v0.2.8

Run it:

docker run --rm -p 8080:8080 ghcr.io/huzefaaa2/terraform-guardrail:v0.2.8

Docker Compose Stack (Local Dev)

Bring up API + Streamlit UI + policy registry:

docker compose up --build

Enable optional analytics (Prometheus + Grafana):

docker compose --profile analytics up --build

Service URLs:

• API: http://localhost:8080
• Streamlit UI: http://localhost:8501
• Policy registry (static): http://localhost:8081
• Prometheus (analytics profile): http://localhost:9090
• Grafana (analytics profile): http://localhost:3000 (admin / guardrail)

flowchart LR
    subgraph ComposeStack[Docker Compose Stack]
        UI([Streamlit UI])
        API([REST API])
        REG[(Policy Registry)]
        PROM[[Prometheus]]
        GRAF[[Grafana]]
    end
    UI --> API
    API -.-> REG
    API --> PROM
    PROM --> GRAF

    classDef core fill:#e8f5e9,stroke:#2e7d32,stroke-width:1px,color:#1b5e20;
    classDef optional fill:#fff3e0,stroke:#ef6c00,stroke-width:1px,color:#e65100;
    class UI,API,REG core;
    class PROM,GRAF optional;

Release Links

• PyPI: https://pypi.org/project/terraform-guardrail/
• GitHub Releases: https://github.com/Huzefaaa2/terraform-guardrail/releases
• Container Image: https://github.com/Huzefaaa2/terraform-guardrail/pkgs/container/terraform-guardrail
• Release history: RELEASE.md

Deployment Guide

See docs/streamlit_cloud.md for a detailed Streamlit Cloud walkthrough.

Release Checklist

• Update version in pyproject.toml.
• Update RELEASE_NOTES.md and CHANGELOG.md.
• Commit changes and push to main.
• Create and push a tag: git tag -a vX.Y.Z -m "vX.Y.Z" then git push origin vX.Y.Z.
• Confirm GitHub Actions release workflow completed successfully.

Changelog Automation

This repo uses git-cliff to generate CHANGELOG.md.

git cliff -o CHANGELOG.md

Or run:

make changelog

Release Helpers

make release-dry VERSION=0.2.8
make version-bump VERSION=0.2.8

MCP tools (current)

• scan_terraform: Run compliance checks over a path and optional state file.
• get_provider_metadata: Fetch provider metadata from Terraform Registry.
• generate_snippet: Generate Terraform snippets for common resources.

License

MIT