✨ Gemini + MCP Playground

An AI agent that uses Google Gemini and the Model Context Protocol (MCP) to securely interact with data and tools.

Built with Streamlit, Gemini, Agno, and a custom MCP server — no Docker needed!

🏆 Why This Project?

This showcases AI Engineering skills:

Related MCP server: sqlite-mcp

✨ Features

• Natural Language Queries — Ask questions in plain English about a sample e-commerce database

• Custom MCP Server — A local server that translates AI requests into safe database queries

• 🔒 Security Guardrails — Only SELECT queries allowed; all modifications blocked

• Multiple Export Formats — Download results as CSV, JSON, or Markdown with one click

• AI-Assisted Export — Just say "export this as CSV" and the AI handles it

• Tool Transparency — See exactly which tools the AI calls and what SQL it writes

🧠 How It Works

You: "Show me all products over $50"
                │
                ▼
┌───────────────────────────────────────┐
│   Streamlit Web App (github_agent.py)  │
│                                       │
│  ┌─────────────────────────────────┐  │
│  │  Gemini (Google AI)             │  │
│  │  • Understands your question    │  │
│  │  • Decides which tool to call   │  │
│  │  • Formats the answer           │  │
│  └──────────┬──────────────────────┘  │
│             │ MCP Protocol             │
│             ▼                          │
│  ┌─────────────────────────────────┐  │
│  │  MCP Server (db_mcp_server.py)   │  │
│  │  • Validates query (read-only?) │  │
│  │  • Runs SELECT on SQLite DB     │  │
│  │  • Returns formatted results    │  │
│  └──────────┬──────────────────────┘  │
│             │                          │
│             ▼                          │
│     ┌──────────────┐                  │
│     │  store.db    │  (SQLite file)    │
│     └──────────────┘                  │
└───────────────────────────────────────┘

🚀 Quick Start

Requirements

• Python 3.8+

• Gemini API Key — Get one free at aistudio.google.com/apikey

No Docker. No GitHub token. No OpenAI key.

Installation

# 1. Install dependencies
pip install -r requirements.txt

# 2. Generate the sample database
python seed_db.py

# 3. Start the app
streamlit run github_agent.py

Usage

1. Enter your Gemini API key in the sidebar (get one free at aistudio.google.com/apikey)

2. Type a question about the store data — e.g., "Show me all products under $50"

3. Click "Run Query" and watch the AI work!

4. Export results using the download buttons below the response

💬 Example Queries

Try asking the AI:

🔍 "Show me all products sorted by price"
🔍 "Which customers have placed the most orders?"
🔍 "What's the total revenue from last month?"
🔍 "Show me orders that haven't shipped yet"
🔍 "Export all products as CSV"
🔍 "How many customers do we have from each city?"

📁 Project Structure

🗄️ Database Schema

The sample database (store.db) contains 5 tables with 30 orders, 25 products, and 10 customers:

🔒 Security Guardrails

The MCP server has layers of protection:

1. Keyword blocking — Queries starting with DELETE, DROP, INSERT, UPDATE, etc. are rejected

2. Multi-statement detection — Multiple SQL statements separated by ; are individually checked

3. Result limiting — Maximum 100 rows returned per query

4. Read-only export — Export tool also validates queries before writing files

🛠️ Tech Stack

📝 License

This project is for educational purposes. Built as a demonstration of AI Agent + MCP architecture.