Atlas-G Protocol

Provides a machine-readable portfolio server with governance, live audit logs, and real-time Thought-Action visualization for AI development environments.

python

2

GitHub Stars

python

Language

2 months ago

First Indexed

3 weeks ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

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

Atlas-G Protocol is a compliance-grade MCP server that exposes a machine-readable portfolio through an autonomous agent. It enables real-time governance, live auditing, and seamless integration with AI development environments, making it practical to validate engineering practices and compliance checks in live interactions.

How to use

To use Atlas-G Protocol, connect your MCP client to the MCP server instance and run the provided local command to launch the server core. Your client can then send queries, request verification, and observe governance-filtered responses in real time. The system streams internal checks and displays a Thought-Action loop for transparency.

How to install

Prerequisites you need before installation:

Python 3.11+

Install the server and dependencies by following these steps:

# Clone the repository
# Note: adjust the path if your directory differs
git clone <repository-url> Atlas-G Protocol
cd Atlas-G Protocol

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

# Copy environment template and edit as needed
cp .env.example .env
# Edit .env to set YOUR_GOOGLE_API_KEY with your Gemini API key
```} ,{

Additional configuration and notes

Security and data governance are integral to Atlas-G Protocol. It applies privacy-by-design, stores proprietary information in a private data path, and validates all AI responses against a local knowledge graph to mitigate hallucinations. You should ensure your environment variables reflect your security needs and that you understand how governance filters apply to generated responses.

Project structure highlights relevant to running the MCP server locally include the backend module, the mcp_server component, and the data source used for the knowledge graph. The server exposes a local command to start the MCP process and requires proper credentials to access external services when applicable.

Troubleshooting and tips

If you encounter connection issues, verify that the MCP server process is running and that your client is configured to reach the correct runtime. Check logs for governance validation messages to confirm that the local knowledge graph is being used for response filtering.

When deploying to cloud infrastructure, ensure you provide the required API keys and set environment variables as described in the deployment instructions to enable Gemini access and governance checks.

Examples and workflows

Typical workflow: start the MCP server, connect your AI development environment, issue queries to retrieve resume-related knowledge, perform cross-checks with the provided tools, and monitor the live audit stream to confirm compliance checks are being applied in real time.

Available tools

query_resume

Semantic search over resume knowledge graph to locate relevant information quickly.

verify_employment

Cross-reference employment claims against known records to validate accuracy.

audit_project

Deep-dive into project architecture and evaluate governance considerations.