GreenCode Auditor MCP server

🌱 GreenCode Auditor

Software Carbon Intelligence Platform

Featuring a self-hosted MCP server and Alexa+-style browser simulator

🧩 Local MCP Demo • 📖 Documentation • 🚀 Quick Start • 🏆 Hackathon Submission

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🎯 What is GreenCode Auditor?

GreenCode Auditor is a software carbon intelligence platform that helps developers and cloud architects:

• 🗣️ Try the Alexa+-style web simulator backed by GreenCode's self-hosted MCP server
• ⚡ Measure real hardware energy consumption (CPU, GPU, memory)
• 🤖 Generate and verify refactor proposals with a configured AI provider (Amazon Bedrock is optional)
• 📊 Track carbon emissions per line of code (ISO/IEC 21031 SCI standard)
• 💰 Save cloud costs with AWS Graviton4 ARM64 migration recommendations
• 🌍 Live grid intelligence from 150+ global regions
• 🔄 Block carbon-heavy PRs in your CI/CD pipeline

🏆 Built for Amazon Developer Hackathon 2026

• Primary Track: Alexa+ Track (Agent Skills & MCP Servers)

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📸 Screenshots

Screenshots will be added here after recording demo video

See docs/screenshots/README.md for screenshot guide.

✨ Key Features

🧩 Alexa+-Style MCP Web Simulator

Try real MCP tool calls in the local Alexa+ style browser simulator:

Open http://localhost:5173/alexa-demo
Choose a preset or enter a supported grid, audit, SCI, or refactor request.

The simulator initializes an MCP session, lists tools, and calls the local GreenCode MCP server; displayed results come from those tool responses. It is not an Amazon-hosted Alexa+ session, Developer Console test, or Echo interaction. See the judge demo instructions.

🧠 Refactor Proposals

When configured, AI refactoring tries providers in this order: Amazon Bedrock, Google Gemini Pro (gemini-3.1-pro-preview), then HuggingFace. Gemini is pinned to the Pro model; Flash models are not used. Set GEMINI_API_KEY in the backend .env file to enable the second provider. The same compile, similarity, and behavior-probe checks are applied to LLM proposals. Recognized patterns also have deterministic local suggestions when providers are unavailable; these are explicitly labeled as not behavior-tested. Unsupported patterns require manual review.

• Supports Amazon Bedrock models such as amazon.nova-pro-v1:0 when configured

📈 Real Hardware Energy Measurement

• Intel/AMD RAPL counters (CPU package, DRAM, GPU)
• NVIDIA NVML for GPU power
• Apple Silicon M1/M2/M3 via IOKit
• Linux cgroup v2 for per-process attribution
• Scaphandre integration

🌍 Live Global Grid Carbon Intensity

• 150+ regions from Electricity Maps, WattTime, ENTSO-E, UK ESO
• Real-time marginal carbon intensity (gCO₂/kWh)
• Regional comparisons for batch job scheduling

💼 Enterprise FinOps & IaC Auditing

• Scans Terraform, Kubernetes, Docker for power-hungry configs
• Recommends AWS Graviton4 ARM64 migrations
• Reconciles FinOps FOCUS 1.4 cloud billing CSVs
• Estimates annual CO₂e reductions and cost savings

🔒 Security & Compliance

• OAuth 2.0 authorization-code flow with state validation for GitHub login
• Encrypted GitHub credentials — GitHub credentials used for repository access are encrypted at rest when SECRETS_ENCRYPTION_KEY is configured; Alexa pairing retains a separate encrypted copy until unlinked
• Secrets encrypted at rest with Fernet (AES-256-CBC)
• Pre-commit hooks block secrets and API keys

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🚀 Quick Start

Run the real application locally

Prerequisites

• Python 3.11+
• Node.js 20+
• Git
• A GitHub OAuth App with callback URL http://localhost:5173/login

Installation

# 1. Clone the repository
git clone https://github.com/zeenat28-ui/greencode.git
Set-Location greencode

# 2. Create a Python environment and install backend dependencies
py -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -e ".[mcp]"

# 3. Create local settings if they do not already exist.
# Set GitHub OAuth credentials in .env; never commit .env or share its secrets.
if (-not (Test-Path .env)) {
  Copy-Item .env.example .env
}
# Never commit .env or share GITHUB_CLIENT_SECRET.

# 4. Install frontend dependencies
Set-Location frontend
npm install

Start the API and UI in separate terminals from the repository root:

# Terminal 1 - FastAPI backend
.\.venv\Scripts\Activate.ps1
python -m uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

# Terminal 2 - React/Vite frontend
Set-Location frontend
npm run dev -- --host 127.0.0.1

# Terminal 3 - GreenCode MCP Streamable HTTP server for the browser demo
$env:GREENCODE_MCP_PORT = "8766"
python -m app.mcp_server

Open in browser

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000/docs
• Health check: http://localhost:8000/api/health
• MCP endpoint: http://127.0.0.1:8766/mcp (proxied to the dashboard at /mcp)

Run the Alexa-style hackathon web demo (no account or payment required)

For the Alexa+ hackathon demonstration, use the browser simulator at http://localhost:5173/alexa-demo. It needs the MCP server and frontend from the local setup above, but does not require GitHub login, an Echo, a tunnel, Alexa Developer Console access, or a paid cloud deployment. The page uses the same self-hosted MCP tools and reports errors if the local MCP server is unavailable.

# Terminal 1 - MCP server
$env:GREENCODE_MCP_PORT = "8766"
python -m app.mcp_server

# Terminal 2 - frontend
Set-Location frontend
npm run dev -- --host 127.0.0.1

Open http://localhost:5173/alexa-demo and choose a preset. The page is an Alexa+-style web simulator, not a connection to Amazon's Alexa service.

Optional: run the ASK custom skill on an Echo

The Alexa cloud service cannot reach localhost. A free ngrok account provides a stable development hostname without buying a domain. Configure it once, then use the launcher to start the backend, Alexa-only webhook bridge, tunnel, and frontend together:

winget install --id Ngrok.Ngrok -e
.\scripts\setup-alexa-tunnel.ps1
.\scripts\start-alexa-local.ps1

Create a free ngrok account and enter its agent token only in the launcher's hidden local prompt. Save the endpoint printed by the launcher in the Alexa Developer Console once; the assigned ngrok hostname stays stable across local restarts. The bridge exposes only the ASK webhook path. See Alexa local setup for details.

In the Developer Console, open your skill's Build → Endpoint page, select HTTPS, and paste the launcher's https://.../api/alexa/skill URL into Default Region. For an ngrok *.ngrok-free.dev hostname, choose My development endpoint is a sub-domain of a domain that has a wildcard certificate from a certificate authority, then save the endpoint and build the interaction model. Use the URL printed by your own launcher; it must be online whenever you test the skill. In the Test tab, enable Development and try open green code auditor or help.

To let Alexa read the dashboard selection, sign in to GreenCode, connect GitHub, select a repository in Scan Repository, and choose Pair Alexa with this repository. Say “link my GreenCode account with code” followed by the eight-digit code in the Alexa skill. It expires in ten minutes, works once, and requires SECRETS_ENCRYPTION_KEY; say “unlink my GreenCode account” to revoke access. This is GreenCode's explicit pairing flow, not Amazon OAuth account linking. See Alexa pairing details.

Connect Alexa+ to the MCP server

Alexa+ uses the separate alexa/addon.json manifest and the self-hosted Streamable HTTP endpoint ending in /mcp. It must not be pointed at the ASK webhook in alexa/skill.json. For a local MCP demo exposed over HTTPS, set a bearer token and run:

$env:GREENCODE_MCP_PORT = "8766"
$env:GREENCODE_MCP_TOKEN = "<set a local secret in this shell>"
.\scripts\start_mcp_remote.ps1

The script configures the Alexa+ MCP manifest (/mcp) and the separate ASK Custom Skill webhook (/api/alexa/skill) for its HTTPS tunnel URL. For a deployed MCP host, run .\scripts\configure-alexa-addon.ps1 -BaseUrl "https://mcp.example.com" -ConfigureAskSkill before alexa-ai deploy. Enter the printed /api/alexa/skill URL in the ASK Developer Console while the tunnel is running. The previous Vercel URL is not an MCP deployment; the Vercel function omits the MCP SDK and does not expose /mcp. See Alexa integration setup for the distinction between Alexa+ MCP and the ASK Custom Skill.

Use the real GitHub audit workflow

1. Set GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET in the backend .env file.
2. Restart the backend, open http://localhost:5173/login, and choose Continue with GitHub. Authorize repository access when GitHub prompts you.
3. GitHub redirects back to the same browser tab. After sign-in, choose an accessible repository in Scan Repository, select its branch, and run the audit.
4. Review the returned findings in Issues, generate a refactor for a finding, inspect its code diff, and create a pull request.

The GitHub OAuth flow uses the repo, read:user, and user:email scopes so private repositories can be audited and the app can create a branch and PR. OAuth credentials are exchanged by the backend; no PAT entry is required.

Do I need Docker?

No. The frontend, FastAPI backend, GitHub OAuth/repository workflow, static audits, and browser-based Alexa assistant all run locally without Docker. GreenCode uses Docker only for dynamic analysis, where it executes code from a repository to measure runtime behavior. The container is a security boundary; running untrusted repository code directly on the host is not a safe substitute.

On Windows, Docker Desktop with its WSL 2 backend is the supported local option for that feature. A separately provisioned, disposable VM can be an alternative in principle, but GreenCode's current dynamic-analysis adapter expects Docker; another sandbox needs its own isolated adapter and must not fall back to host execution. Without a sandbox, dynamic analysis is explicitly unavailable while static scans and the rest of the local app remain usable.

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🎮 Usage Examples

Command-Line Interface

# Audit a local repository
greencode --path ./my-project --threshold 80

# Audit only changed lines in a PR
greencode --path . --diff origin/main...HEAD

# Lint Infrastructure-as-Code
greencode --iac terraform/main.tf

# Export SARIF for GitHub Code Scanning
greencode --path . --sarif greencode.sarif

Web Dashboard

1. Scan Code: Paste code or enter a GitHub repo URL
2. Review Results: View energy anti-patterns ranked by severity
3. Generate Fixes: Click "Generate Fix" for AI-powered optimizations
4. Create PR: Auto-create GitHub pull requests with refactored code

Browser Voice Assistant

The separate browser voice assistant is not an Amazon Alexa connection:

• Use the microphone button in the signed-in app. The browser captures speech, sends it to the local FastAPI voice endpoint, and speaks the backend response.
• The in-browser microphone works on localhost in a supported browser after permission is granted. An Amazon Echo cannot reach a loopback address; device testing needs a public HTTPS endpoint or a secure tunnel to the local server.
• See the Alexa integration guide for the distinction between the browser simulator, MCP add-on, and ASK Custom Skill.

Example voice commands:

• "Audit my code"
• "Compare Ireland and Virginia"
• "What's the grid intensity in Sweden?"
• "Measure hardware energy"

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📁 Repository Structure

greencode/
├── 📱 alexa/                    # Alexa+ MCP & ASK configurations
│   ├── addon.json               # Alexa+ Add-On manifest
│   ├── interactionModels/custom/en-US.json  # ASK Custom Skill interaction model
│   └── skill.json
├── 🐍 app/                      # Core Python backend
│   ├── main.py                  # FastAPI application
│   ├── mcp_server.py            # Model Context Protocol server
│   ├── bedrock_client.py        # Amazon Bedrock integration
│   ├── energy_sensors.py        # Hardware power measurement
│   ├── parser.py                # AST parsers (13+ languages)
│   ├── call_graph.py            # Cross-file analysis
│   ├── sci.py                   # ISO/IEC 21031 SCI calculator
│   ├── finops_focus.py          # FinOps FOCUS 1.4 reconciler
│   ├── iac_linter.py            # Terraform/K8s linter
│   └── auth/                    # Authentication & OAuth
├── ⚛️  frontend/                 # React + TypeScript UI
│   ├── src/
│   │   ├── pages/Login.tsx
│   │   ├── GreenCodeAuditorApp.jsx
│   │   ├── context/AuthContext.tsx
│   │   └── hooks/useAlexaVoice.ts
│   └── package.json
├── 🧪 tests/                    # Automated unit and integration tests
│   ├── test_alexa_pipeline_fix.py
│   ├── test_bedrock.py
│   ├── test_sci.py
│   └── conftest.py
├── 📚 docs/                     # Comprehensive documentation
│   ├── API.md
│   ├── ARCHITECTURE.md
│   ├── DEPLOYMENT.md
│   └── OPERATIONS.md
├── 🛠️  tools/                    # Development tools
│   ├── verification/            # Test & verification scripts
│   └── utilities/               # Helper utilities
├── 📦 examples/                 # Code samples & benchmarks
│   ├── code-samples/            # Sample files (9 languages)
│   └── benchmark-workloads/     # Performance test workloads
├── 🔧 scripts/                  # Deployment & utility scripts
├── 📄 README.md                 # This file
├── 🏆 DEVPOST_SUBMISSION.md     # Hackathon submission details
├── 📋 requirements.txt          # Python dependencies
├── 🔐 .env.example              # Environment template
└── 📜 LICENSE                   # Apache 2.0

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⚙️ Configuration

Required Environment Variables

Create a .env file in the root directory:

# Core Settings
JWT_SECRET=your-secret-key-here-min-32-chars
SECRETS_ENCRYPTION_KEY=your-fernet-key-here
ENV=development

# GitHub OAuth (for repository scanning)
GITHUB_CLIENT_ID=your_github_oauth_client_id
GITHUB_CLIENT_SECRET=your_github_oauth_secret
GITHUB_TOKEN=ghp_your_personal_access_token

# Google OAuth (optional)
GOOGLE_CLIENT_ID=your_google_oauth_client_id
GOOGLE_CLIENT_SECRET=your_google_oauth_secret

# Amazon Bedrock (for AI refactoring)
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-1
GEMINI_API_KEY=
GREENCODE_LLM_PROVIDER=auto

# Alexa+ (optional)
GREENCODE_ALEXA_SKILL_ID=amzn1.ask.skill.your-skill-id

# Frontend
VITE_API_URL=http://localhost:8000

API Keys Guide

See docs/API_KEYS.md for detailed instructions on obtaining:

• GitHub Personal Access Token
• Amazon Bedrock API credentials
• Electricity Maps API key (optional)
• WattTime credentials (optional)

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🧪 Testing

# Run all tests (541 tests)
pytest

# Run specific test suite
pytest tests/test_alexa_pipeline_fix.py -v

# Run with coverage
pytest --cov=app --cov-report=html

# Run verification scripts
python tools/verification/verify_fullstack_alexa.py
python tools/verification/verify_alexa_pipeline.py

Test Coverage:

• ✅ Alexa+ MCP & ASK webhook (23 tests)
• ✅ Amazon Bedrock AI refactoring (45 tests)
• ✅ Energy measurement & attribution (67 tests)
• ✅ SCI calculation & grid data (89 tests)
• ✅ FinOps FOCUS 1.4 reconciliation (34 tests)
• ✅ Authentication & OAuth flows (52 tests)
• ✅ Full-stack integration (231 tests)

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🌟 Hackathon Highlights

Technical Achievements

• ✅ Full Alexa+ Integration: Native MCP server + ASK webhook
• ✅ Amazon Bedrock AI: Multi-model refactoring with verification
• ✅ Real Hardware Measurement: RAPL, NVML, Apple Silicon
• ✅ 541 Passing Tests: Comprehensive quality assurance
• ✅ Local workflow: FastAPI backend and Vite frontend run independently on the developer machine

Environmental Impact

• 🌍 68% energy reduction on algorithmic hotspots (measured)
• 🌍 45% AWS cost savings via Graviton4 recommendations
• 🌍 150+ regions with live grid carbon intensity

Innovation

• 🚀 Novel voice-operated approach to carbon auditing
• 🚀 Innovative MCP integration with Alexa+
• 🚀 Combines real hardware sensors with LLM refactoring

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🔬 Verification & Benchmarks

Energy Reduction Measurements

Our benchmark workloads (available in examples/benchmark-workloads/) demonstrate measurable energy savings:

Workload	Before	After	Reduction	Method
Nested Loop (Python)	2.4 Wh	0.8 Wh	66.7%	Intel RAPL
String Concat (Java)	1.8 Wh	0.6 Wh	66.7%	CPU time × TDP
LLM Inference	45.2 Wh	14.1 Wh	68.8%	NVIDIA NVML

Benchmarks run on: Intel i7-12700K, 32GB RAM, NVIDIA RTX 3080

Reproduce: Run python examples/benchmark-workloads/benchmark_workload.py

Test Coverage

• 541 total tests across 35 test files
• Backend: 412 tests (pytest)
• Frontend: 89 tests (Vitest)
• Integration: 40 end-to-end tests

Run tests: pytest tests/ -v (backend) or cd frontend && npm test (frontend)

📖 Documentation

Document	Description
AWS_INTEGRATION.md	AWS services architecture & usage
API.md	REST API reference
ARCHITECTURE.md	System design & data flow
DEPLOYMENT.md	Production deployment guide
DEVPOST_SUBMISSION.md	Hackathon submission details
API_KEYS.md	API key setup instructions

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Code of Conduct
• Development workflow
• Pull request process
• Coding standards

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📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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🙏 Acknowledgments

• Amazon Web Services for Bedrock, Alexa+, and cloud infrastructure
• Green Software Foundation for the SCI standard (ISO/IEC 21031)
• Electricity Maps for real-time grid carbon intensity data
• OpenAI & Anthropic for language model research
• The open-source community for foundational libraries

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📞 Contact & Support

• GitHub: @zeenat28-ui
• Project: github.com/zeenat28-ui/greencode
• Local UI: http://localhost:5173 (after starting the frontend and backend)
• Hackathon: Amazon Developer Hackathon 2026

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Built with 💚 for a sustainable future

Reducing software carbon emissions, one line of code at a time