Dmcps

🛡️ DMCPS (Docker Model Context Protocol Secured)

DMCPS V3 is the ultimate enterprise-grade sandbox for AI coding agents, completely re-engineered to run natively on Google Cloud Run. By utilizing Google's serverless container infrastructure, you can give AI agents (like Claude Desktop, Cursor, or Gemini) full terminal and filesystem execution without ever risking your host machine.

🌟 Hackathon Exclusive Features

1. Native Google Cloud Run Architecture: Zero-friction automated deployments via cloudbuild.yaml. Designed from the ground up to utilize Google's serverless containers.
2. Product Hunt PKCE OAuth Flow: The web dashboard is secured using Product Hunt's official API. Authenticate your makers and manage your AI sandbox instantly using your Product Hunt account.
3. Enterprise Open Source (BSL-1.1): Codebase is fully open for developers to inspect the kernel-level protections and deploy to their own GCP environment, while legally protecting commercial/enterprise use.
4. Kernel-Level Locks: Non-root Alpine execution with dropped Linux capabilities (cap_drop: ALL).
5. Absolute Root Illusion: The AI is assigned a direct root user within the disposable container, tricking it into feeling absolute freedom without any "Permission denied" frustrations, keeping it from getting stuck in looping errors.

🚀 Getting Started on Google Cloud

Deploying DMCPS V3 is incredibly simple using Google Cloud Build.

1. Navigate to Cloud Build Triggers in your Google Cloud Console.
2. Click Connect Repository and link this GitHub repository.
3. Set the trigger to watch the v3-beta branch.
4. Point the configuration to the included /cloudbuild.yaml file.
5. Click Run.

Google Cloud Build will automatically build the container, push it to your Container Registry, and deploy it as a highly scalable Google Cloud Run service!

🔒 Security & Dashboard Whitelisting

This is built as a robust Node.js/Express backend daemon, featuring a "military-grade" secured dashboard to strictly manage which directories the AI is allowed to touch.

• Strict Whitelisting: The AI cannot read, write, or execute commands outside of directories explicitly whitelisted via the web dashboard. (Directory traversal attempts like ../ are mathematically blocked).
• Product Hunt Integration: Secure login restricted to Product Hunt makers using PKCE OAuth 2.0 flow.
• Auto-Generated API Keys: Connect to your MCP server using a dynamically generated Bearer token to ensure only authorized agents can execute tools on your server.
• Pre-installed AI Toolkit: Foundational tools (git, python3, curl, bash, make, jq) are pre-baked into the image so the AI is immediately ready to work.

🛠️ MCP Tools Exposed to the AI

Once authenticated and restricted to a whitelisted folder, the AI has access to:

1. ph_graphql_query - Run deep analytics queries against the Product Hunt API.
2. read_file - Read text from a file.
3. write_file - Write content to a file.
4. list_directory - List all files in a folder.
5. run_shell_command - Execute terminal commands strictly within the isolated workspace.

🧪 Local Testing

You can easily test the entire stack locally or on an AWS instance before pushing to Google Cloud Run:

git clone -b v3-beta https://github.com/RMAAPK/dmcps.git

cd dmcps git checkout v3-beta docker compose up -d --build

*Note: Ensure you port-forward to `https://localhost:5000` to properly test the Product Hunt OAuth Redirect URI.*