maveric-mcp-mini

mH-13/maveric-mcp-mini

3.1

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Maveric MCP Mini is a real-time network tower monitoring system utilizing Model Context Protocol (MCP) for intelligent log processing and AI-powered analysis.

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Maveric MCP Mini

Real-time network tower monitoring system using Model Context Protocol (MCP) for intelligent log processing and AI-powered analysis.

Architecture

┌─────────────────┐  MCP Protocol  ┌─────────────────┐  MongoDB Query ┌─────────────────┐
│                 │  (stdio/JSON)  │                 │  (PyMongo)     │                 │
│  Log Generator  │──────────────▶│   MCP Server    │──────────────▶│    MongoDB      │
│                 │                │                 │                │                 │
│ • Simulates     │                │ Tools:          │                │ • Time-series   │
│   cell towers   │                │ • write_logs    │                │   log storage   │
│ • ON/OFF status │                │ • fetch_logs    │                │ • Automatic     │
│ • Batch writes  │                │ • summarize     │                │   indexing      │
└─────────────────┘                └─────────────────┘                └─────────────────┘
                                             │
                                             │ API Call
                                             ▼
                                    ┌─────────────────┐
                                    │   Groq LLM      │
                                    │                 │
                                    │ • Natural lang  │
                                    │   summaries     │
                                    │ • Anomaly det   │
                                    └─────────────────┘

Data Flow

  1. Log Generation: Simulates cell tower status changes (ON/OFF)
  2. MCP Communication: Generator calls MCP server tools via stdio protocol
  3. Data Storage: MCP server writes logs to MongoDB with automatic indexing
  4. Analysis: Fetch logs and generate AI summaries using Groq LLM
  5. Visualization: Jupyter notebooks provide analytics dashboards

Project Structure

maveric-mcp-mini/
├── src/
│   ├── common/              # Shared utilities
│   │   ├── db.py            # MongoDB connection & indexing
│   │   └── models.py        # Pydantic data models
│   ├── mcp_server/          # MCP server implementation
│   │   ├── server.py        # FastMCP server with tools
│   │   └── summarizers/     # AI summarization
│   ├── generator/           # Log simulation
│   └── clients/             # Client utilities
├─ enhanced_analytics.ipynb  # Advanced analytics dashboard
├─ simple_analytics.ipynb    # Basic analytics (no pandas)
├─ docker-compose.yml        # MongoDB setup
└─ requirements.txt          # Dependencies

Core Components

MCP Server

  • FastMCP framework for tool exposure
  • Tools: write_logs, fetch_logs, summarize_recent
  • Transport: stdio protocol for client communication

Data Models

class CellLog(BaseModel):
    cell_id: int                    # Tower identifier
    status: Literal["ON", "OFF"]    # Current status
    ts: datetime                    # Timestamp (UTC)
    run_id: int                     # Batch identifier
    cluster: Optional[str]          # Geographic cluster

Database

  • MongoDB for time-series log storage
  • Automatic indexing on timestamp fields
  • TTL support for log expiration

Key Features

  • Real-time Simulation: Configurable cell tower behavior
  • MCP Protocol: Modern tool-calling interface
  • AI Analysis: Groq LLM for natural language summaries
  • Visual Analytics: Jupyter dashboards with SLA tracking
  • Scalable Storage: MongoDB with automatic indexing

See for complete setup guide.

License

MIT License - see LICENSE file for details.