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. <img width="947" alt="image" src="https://github.com/user-attachments/assets/51b19e66-e780-4281-af02-b05e23690e1d" /> --- ## 📁 Project Structure ```bash . ├── 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 ```bash git clone https://github.com/your-repo/tax-alert-chatbot.git cd tax-alert-chatbot ``` ### 2. Create and Activate Virtual Environment ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` ### 3. Install Dependencies ```bash 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: ```bash GOOGLE_API_KEY=your_google_api_key ALERTS_DB=dummy_tax_alerts.db ``` ## 🗃️ SQLite Schema ```bash 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 ```bash python server.py ``` or ```bash python server.py --transport stdio ``` Make sure your .env contains a valid path to dummy_tax_alerts.db. ## 💬 Running the Client (Chat UI) ```bash 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: ```bash "Show me tax alerts from 2024 in California" ``` ### Agent Response (Tool Call): ```bash 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.