Tax Alert Chatbot MCP Server

📊 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.

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

A server that powers an interactive chatbot for querying and managing tax alerts in a SQLite database using Google Gemini models and LangGraph's REACT agent framework.

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