# 🤖 MCP Enterprise AIOps Agent & Gateway

A Production-Grade **Model Context Protocol (MCP)** implementation in Python powered by **Groq Llama-3.3-70B** and **Streamlit Web UI**.

This project functions as an **Autonomous Self-Healing AIOps Engine** that monitors system metrics, detects memory/CPU threshold breaches, and dynamically executes remediation tools to restore server health without human intervention.

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## 🌟 Key Features

- **Autonomous LLM Agent**: Leverages Anthropic's Model Context Protocol (MCP) and Groq LLM Function Calling.
- **Self-Healing Architecture**: Automatically detects high CPU/RAM usage (e.g. >80%) and triggers remediation scripts down to safe levels (~35%).
- **Dynamic Tool Router**: Centralized registry mapping LLM tool requests to python functions.
- **Zero-Trust Security**: Token authentication middleware and Human-in-the-Loop authorization hooks for sensitive operations.
- **Multi-Interface Support**: Operates via Streamlit Web UI & CLI Terminal with Winston-style Python logging.

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## 🛠️ Project Architecture

mcp-enterprise-aiops-agent/
├── .env
├── .gitignore
├── requirements.txt
├── README.md
├── app_gui.py
└── src/
    ├── __init__.py
    ├── security/
    │   ├── __init__.py
    │   ├── auth.py              # Zero-Trust JWT/Token Validation
    │   └── privacy_gateway.py  # Presidio-style PII Redaction & Data Masking
    ├── tools/
    │   ├── __init__.py
    │   ├── system_tool.py      # System Health Monitoring & Self-Healing
    │   └── external_api_tool.py# Microservice Tool Integration
    ├── core/
    │   ├── __init__.py
    │   ├── policy_engine.py    # OPA-style Policy Enforcement & Human-in-the-Loop
    │   ├── tool_router.py      # Dynamic MCP Tool Registration & Execution
    │   └── llm_agent.py        # Multi-turn Autonomous Agent Loop
    └── utils/
        ├── __init__.py
        └── logger.py           # Structured Audit Logging (OpenTelemetry style)vice Data)
```