🧠 Agentic-AI-MCP-Query-Brain

An intelligent, agentic system built with Model Context Protocol (MCP) that transforms natural language queries into SQL, executes them against a database, and returns human-friendly results. Powered by modular microservices, Redis memory, and PostgreSQL for robust, context-aware querying.

📌 Overview

This project enables you to ask questions in plain English and receive structured data answers. It does so using:

• A modular MCP architecture for agent-to-tool communication
• FastAPI microservices hosting API endpoints
• Redis memory for storing conversational context
• OpenAI / LLM integration for generating SQL
• PostgreSQL backend for executing queries
• Docker + NGINX setup for production scalability

🧠 Tech Stack

📁 Project Structure

Agentic-AI-MCP-Query-Brain/
├── agent/                     # Core MCP agent logic
├── api_client/                # Client side communication logic
├── api_service/               # FastAPI based endpoints
├── docker/                    # Dockerfiles & container setup
├── memory/                    # Redis memory and context logic
├── models/                    # Data models & schema definitions
├── sdk/                       # MCP SDK & router utilities
├── services/                  # Tool registry and helper services
├── sql_tool/                  # SQL execution, explanation & validation
│
├── main.py                     # FastAPI entry point
├── main_stdio.py               # MCP host via stdio runner
├── requirements.txt            # Python dependencies
├── docker-compose.yml          # Multi-container orchestration
├── nginx.conf                  # NGINX configuration
└── README.md                   # This documentation

🧩 Key Tools & Modules

• OpenAITool — Converts natural language queries to SQL
• SQLTool — Executes SQL on PostgreSQL securely
• ExplainSQLTool — Converts SQL into readable descriptions
• QueryCacheTool — Caches commonly run queries
• FeedbackLoggingTool — Logs user feedback for model tuning
• NaturalLanguageResponseTool — Turns SQL results into textual responses
• RateLimiterTool — Controls request throughput
• TableSchemaTool — Retrieves schema metadata for better query accuracy

🧠 How It Works

1. User input (natural language) is sent via the frontend or CLI.
2. The MCP Host routes the input to the appropriate tool.
3. OpenAITool generates SQL from the input using LLM reasoning.
4. SQLTool executes the query on PostgreSQL, returning raw results.
5. NaturalLanguageResponseTool translates results into readable form.
6. Redis memory retains conversation context for follow-up queries.

⚙️ Example Configuration Snippet (VS Code / MCP)

Use this example in your MCP setup (sensitive keys masked for security):

{
  "mcpServers": {
    "vartopia-sql-agent": {
      "command": "D:/vartopia/.venv/Scripts/python.exe",
      "args": [
        "-u",
        "D:/vartopia/main_stdio.py"
      ],
      "env": {
        "OPENAI_API_KEY": "sk-proj-********-REDACTED",
        "DB_URL": "postgresql://mcp_postgres_user:********@render.com/mcp_postgres",
        "REDIS_URL": "redis://localhost:6379"
      },
      "transport": "stdio",
      "workingDirectory": "D:/vartopia"
    }
  }
}

▶️ Getting Started

✅ Prerequisites

• Python 3.12+
• PostgreSQL database
• Redis server
• Docker & Docker Compose (optional, but recommended)

🛠 Setup Steps

1. Clone the repository

   git clone https://github.com/Ramneek82810/Agentic-AI-MCP-Query-Brain.git
   cd Agentic-AI-MCP-Query-Brain

1. Install dependencies

   pip install -r requirements.txt

1. Run the FastAPI service

   uvicorn main:app --reload

1. Or start with Docker (multi-container setup)

   docker-compose up --build

🧠 Architecture Flow

User Input
   ↓
MCP Client → MCP Host (FastAPI)
   ↓
Tool Router → [OpenAITool ⇄ SQLTool ⇄ MemoryTool]
   ↓
Redis Memory ↔ PostgreSQL
   ↓
Formatted JSON or Natural Language Response

🧩 Example Use Case

Input:

“Show the top 5 sales by department for the last quarter.”

Pipeline:

• OpenAITool → Generates SQL
• SQLTool → Executes query
• NaturalLanguageResponseTool → Formats the results

Output:

“Here are the top 5 departments by sales last quarter: Electronics, Home, Fashion, Sports, and Toys.”

📈 Future Enhancements

• 🗄 Multi-database support (MySQL, MongoDB)
• 🧠 Custom fine-tuned LLMs for SQL generation
• 🛡 Role-based authentication & access control
• 🤖 Multi-agent orchestration for complex workflows

📄 License

This project is licensed under the MIT License — free to use, modify, and distribute with attribution.