1
Add it to Claude Code
claude mcp add -e "GOOGLE_API_KEY=${GOOGLE_API_KEY}" atlas-g-protocol -- python -m backend.mcp_serverRequired:
GOOGLE_API_KEY2
Make your agent remember this setup
atlas-g-protocol's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Machine-readable portfolio accessible by AI development environments
- Real-time hallucination mitigation via knowledge graph validation
- Live audit log streaming internal compliance checks
- WebSocket streaming for thought-action loop visualization
- Private-by-design architecture using local resume data
Tools 3
query_resumeSemantic search over resume knowledge graphverify_employmentCross-reference employment claimsaudit_projectDeep-dive into project architectureEnvironment Variables
GOOGLE_API_KEYrequiredAPI key for Gemini integrationTry it
→Query the resume to find out if the candidate has experience with strict state management.
→Verify the employment claims listed in the resume against the provided knowledge graph.
→Perform an audit of the project architecture to understand the compliance-grade engineering.
→Can you explain the candidate's experience with hallucination mitigation based on their portfolio?
Original README from MichaelWeed/atlas-g-protocol
Atlas-G Protocol
<a href="https://glama.ai/mcp/servers/@MichaelWeed/atlas-g-protocol"> </a>Agentic Portfolio System - A compliance-grade MCP server that serves as both human and machine-readable portfolio.
🎯 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.txtknowledge 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