📊 Tax Alert Chatbot (MCP-Powered)

An interactive Streamlit-based chatbot that connects to a custom MCP (Model Context Protocol) server. It allows users to query, insert, update, and delete tax alerts stored in a local SQLite database. The app uses LangGraph’s REACT agent framework with Google Gemini models and supports both SSE and STDIO transport modes.

📁 Project Structure

.
├── client.py  # Frontend Streamlit Chat UI
├── server.py # MCP tool & Backend FastMCP SQLite server
├── dummy_tax_alerts.db # SQLite database (if present)
├── .env # Environment variables
├──.venv # virtual environment
└── README.md # Documentation

🚀 Features

🤖 Conversational interface with Google Gemini 1.5 models
🧠 REACT-style reasoning agent via LangGraph
🛠️ Tool execution via MCP server
📄 Query, insert, update, and delete operations on tax alert data
🔄 Real-time responses using SSE or STDIO

🛠️ Tech Stack

Layer	Tools / Frameworks
Frontend	Streamlit, LangGraph, LangChain
Backend	FastMCP, SQLite
LLM Provider	Google Gemini 1.5 Flash / Pro (via LangChain)
Transport	SSE (Server-Sent Events) or STDIO
Runtime	Python 3.10+, venv, python-dotenv

⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-repo/tax-alert-chatbot.git
cd tax-alert-chatbot

2. Create and Activate Virtual Environment

python -m venv venv
source venv/bin/activate         # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt
(Optional: Split into client/requirements.txt and server/requirements.txt if needed.)

4. Configure Environment Variables

Create a .env file in the root folder:

GOOGLE_API_KEY=your_google_api_key
ALERTS_DB=dummy_tax_alerts.db

🗃️ SQLite Schema

CREATE TABLE tax_alerts (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    title TEXT,
    date TEXT,
    jurisdiction TEXT,
    topics TEXT,
    summary TEXT,
    full_text TEXT,
    source_url TEXT,
    tags TEXT,
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

🔧 MCP Server Tools

Tool Name	Description
`query(sql)`	Run `SELECT` queries on the `tax_alerts` table
`insert(...)`	Insert a new tax alert into the database
`update(...)`	Update existing tax alerts based on a condition
`delete(...)`	Delete tax alerts using `WHERE` conditions
`schema_info()`	Return schema and column info of the table

▶️ Running the Server

python server.py

python server.py --transport stdio

Make sure your .env contains a valid path to dummy_tax_alerts.db.

💬 Running the Client (Chat UI)

streamlit run client.py

It will automatically open streamlit localhost:8501 in your browser.

⚙️ Configuration (via Sidebar) Gemini Model: Choose between gemini-1.5-flash or gemini-1.5-pro

Server Mode: Only single server supported

Server Type: SSE or STDIO

Server URL: Required only for SSE mode

Clear Chat / Show Tool Executions: Debug & reset tools

🧪 Sample Interaction

User Input:

"Show me tax alerts from 2024 in California"

Agent Response (Tool Call):

SELECT * FROM tax_alerts WHERE jurisdiction='California' AND date LIKE '2024%'

🧼 Debugging & Notes MCP server must be running before starting the client.

Full traceback is shown in the client if errors occur.

Ensure correct database path in .env.