Atlas-G Protocol

Agentic Portfolio System - A compliance-grade MCP server that serves as both human and machine-readable portfolio.

Python FastAPI Cloud Run MCP

🎯 Overview

Atlas-G Protocol transforms a traditional developer portfolio into an autonomous agent that demonstrates compliance-grade engineering in real-time. Instead of reading about experience with "strict state management" and "hallucination mitigation," users interact with an agent that actively demonstrates these capabilities.

Key Features

MCP Server: Machine-readable portfolio accessible by AI development environments
Governance Layer: Real-time hallucination mitigation via knowledge graph validation
Live Audit Log: Streams internal compliance checks to the UI
WebSocket Streaming: Real-time "Thought-Action" loop visualization
CSP Headers: Configured for DEV.to iframe embedding

🔒 Privacy & Data Governance

The Atlas-G Protocol follows a "Private-by-Design" pattern to ensure sensitive career data isn't leaked in public repositories:

Template Pattern: All proprietary information (work history, PII) is stored in data/resume.txt, which is explicitly excluded from the repository via .gitignore.
resume.template.txt: A sanitized template is provided for open-source users to populate with their own data.
Hallucination Mitigation: The agent's governance layer validates every claim against the local resume.txt knowledge graph before responding.

🏗️ Architecture

┌─────────────────────────────────────────────────────┐ │ Cloud Run Instance │ ├─────────────────────────────────────────────────────┤ │ ┌─────────────────┐ ┌─────────────────────────┐ │ │ │ React Frontend │◄──►│ FastAPI Backend │ │ │ │ (Terminal UI) │ │ - Agent Core │ │ │ └─────────────────┘ │ - Governance Layer │ │ │ │ - MCP Server │ │ │ └───────────┬─────────────┘ │ │ │ │ │ ┌───────────▼─────────────┐ │ │ │ Tools │ │ │ │ - query_resume │ │ │ │ - verify_employment │ │ │ │ - audit_project │ │ │ └─────────────────────────┘ │ └─────────────────────────────────────────────────────┘

🚀 Quick Start

Prerequisites

Python 3.11+
Google Cloud API Key (for Gemini)

Installation

# Clone the repository cd Atlas-G\ Protocol # Create virtual environment python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate # Install dependencies pip install -e ".[dev]" # Copy environment template cp .env.example .env # Edit .env with your GOOGLE_API_KEY

Run Locally

# Start the server uvicorn backend.main:application --reload --port 8080 # Open http://localhost:8080

Run Tests

pytest backend/tests/ -v

🔧 MCP Integration

Connect your AI development environment to the Atlas-G MCP server:

{ "mcpServers": { "atlas-g-protocol": { "command": "python", "args": ["-m", "backend.mcp_server"] } } }

Available Tools

Tool	Description
`query_resume`	Semantic search over resume knowledge graph
`verify_employment`	Cross-reference employment claims
`audit_project`	Deep-dive into project architecture

☁️ Deploy to Cloud Run

gcloud run deploy atlas-g-portfolio \ --source . \ --allow-unauthenticated \ --region us-central1 \ --labels dev-tutorial=devnewyear2026 \ --set-env-vars GOOGLE_API_KEY=your_key_here

📁 Project Structure

Atlas-G Protocol/ ├── backend/ │ ├── __init__.py │ ├── main.py # FastAPI application │ ├── agent.py # Thought-Action loop │ ├── governance.py # Hallucination mitigation │ ├── mcp_server.py # FastMCP wrapper │ ├── config.py # Settings management │ └── tools/ │ ├── resume_rag.py │ └── verification.py ├── frontend/ # React UI (Phase 3) ├── data/ │ └── resume.txt # Knowledge graph source ├── Dockerfile ├── pyproject.toml └── mcp_config.json

🔒 Security

CSP Headers: frame-ancestors 'self' https://dev.to https://*.dev.to
Governance Layer: All AI responses validated against resume data
PII Detection: Automatic filtering of sensitive information
Jailbreak Protection: Pattern-based detection and blocking

📄 License

MIT License - See LICENSE for details.

📢 Credits

Audio: Emergency Alarm.wav by Mozfoo (CC0)

atlas-g-protocol