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atlas-g-protocol

by MichaelWeed
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Python
Remote

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

Related MCP server: MCP Resume Chat Server

🔒 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

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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