🤖 AI Data Analyst — MCP Server

An AI-powered data analysis server built on the Model Context Protocol (MCP) using FastMCP. Gives AI agents a rich, standardized toolset to connect to databases, explore schemas, execute secure SQL queries, profile data, generate interactive visualizations, and manage datasets — all through a single, consistent interface.

📋 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Adding to Claude Desktop
• MCP Tools Reference
• Supported Databases
• Security
• License

✨ Features

🗂 Project Structure

ai-analyst/
├── server.py                    # FastMCP server entry point & tool registration
├── create_test_db.py            # Generates a sample SQLite e-commerce database
│
├── database/
│   ├── connection_manager.py    # SQLAlchemy connection management (singleton)
│   └── schema_loader.py         # Schema inspection & caching
│
├── datasets/
│   └── dataset_manager.py       # CSV / Excel / JSON upload & query engine
│
├── security/
│   └── sql_validator.py         # SQL whitelist validator (SELECT / WITH only)
│
├── tools/
│   ├── analyze_schema.py        # Schema relationship analysis tool
│   ├── connect_database.py      # Database connection tool
│   ├── dataset_tools.py         # Dataset upload / list / query tools
│   ├── generate_chart.py        # Chart generation tool
│   ├── get_schema.py            # Schema fetching tool
│   ├── get_schema2.py           # Schema fetching — plain-text format (optional)
│   ├── profile_table.py         # Data profiling tool
│   ├── run_query.py             # SQL query execution tool
│   ├── sample_rows.py           # Table row sampling tool
│   └── session_tools.py         # Session creation tool
│
├── utils/
│   ├── cache_manager.py         # In-memory TTL cache
│   ├── chart_utils.py           # Plotly chart creation & auto-detection
│   ├── data_profiler.py         # SQL / pandas data profiling engine
│   ├── generate_chart.py        # Chart generation wrapper
│   ├── schema_analyzer.py       # Relationship & index analysis
│   ├── session_manager.py       # UUID session management (singleton)
│   └── sql_generator.py         # LLM-based SQL generation
│
└── logs/                        # Runtime log files

🛠 Prerequisites

• Python 3.11

📦 Installation

1. Clone the repository

git clone <repo-url>
cd ai-analyst

2. Create and activate a virtual environment

python -m venv venv

# macOS / Linux
source venv/bin/activate

# Windows
venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

🚀 Quick Start

(Optional) Generate the sample database

python create_test_db.py

Creates sample_store.db — a ready-to-query SQLite e-commerce database containing customers, products, orders, and order_items tables, preloaded with sample data.

Start the MCP server

python server.py

🖥 Adding to Claude Desktop

1. Open Claude Desktop and go to Settings → Developer
2. Click Edit Config to open claude_desktop_config.json
3. Add the following entry inside "mcpServers":

"AI Data Analyst MCP Server": {
  "command": "<path-to-your-python>",
  "args": [
    "<path-to-project>/server.py"
  ],
  "env": {},
  "transport": "stdio"
}

🖥 Adding to your own chatbot

SERVERS = {
  "AI Data Analyst MCP Server": {
  "command": "<path-to-your-python>",
  "args": [
    "<path-to-project>/server.py"
  ],
  "env": {},
  "transport": "stdio"
  }
}

In your chatbot code, initialize the client:

from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient (SERVERS)

Add in your chatbot's llm tools:

tools = client.get_tools()
llm_with_tools = llm.bind_tools(tools)

Now your chatbot can call any of the MCP tools defined in server.py!

Replace the placeholder values:

1. Save the file and restart Claude Desktop.

🔧 MCP Tools Reference

🗄 Supported Databases

Connect to any SQLAlchemy-compatible database using a standard URI:

# SQLite
sqlite:///path/to/database.db

# PostgreSQL
postgresql://user:password@host:port/dbname

# MySQL
mysql://user:password@host:port/dbname

# Microsoft SQL Server
mssql+pyodbc://user:password@host/dbname?driver=ODBC+Driver+17+for+SQL+Server

🔒 Security

The server enforces strict read-only SQL execution to protect your data.

Allowed statements

• SELECT
• WITH (Common Table Expressions / CTEs)

Blocked keywords

DROP · DELETE · UPDATE · ALTER · INSERT · TRUNCATE · MERGE · GRANT · REVOKE

Additional protections

• Multi-statement queries (;-separated) are rejected outright
• Validation is case-insensitive and uses regex word boundaries to prevent bypass attempts

📄 License

This project is provided as-is for educational and research purposes.