Content
---
# Agentic Appointment Scheduling System
## Overview
The **Agentic Appointment Scheduling System** automates the process of booking, rescheduling, and managing appointments with doctors using an agentic framework.
The system integrates an **MCP (Multi-Capability Protocol)** interface, API-based communication, and an optional dashboard for visualization.
---
## Objectives
* To design an AI-based agent capable of handling appointment-related tasks.
* To demonstrate multi-component interaction through API and MCP servers.
* To enable both programmatic and visual monitoring of appointments.
---
## System Capabilities
The agent can perform the following actions:
* **add_appointment** – Create a new appointment.
* **cancel_appointment** – Cancel an existing appointment.
* **reschedule_appointment** – Modify the time or date of an appointment.
* **get_doctors** – Retrieve the list of available doctors.
* **get_all_appointments** – Display all scheduled appointments.
---
## Project Structure
```
appointment_scheduling_agent/
│
├── as_2/
│ ├── api.py # Core FastAPI server handling API requests
│ ├── dashboard.py # Optional Streamlit dashboard for visualization
│ ├── core/ # Core modules and helper functions
│ ├── data/ # Data storage for appointments and doctors
│ ├── server/ # MCP server implementation
│ ├── requirements.txt # Python dependencies
│ └── __pycache__/ # Compiled cache files
```
---
## Installation
### Step 1: Clone the Repository
```bash
git clone https://github.com/<your-username>/appointment_scheduling_agent.git
cd appointment_scheduling_agent/as_2
```
### Step 2: Create a Virtual Environment
```bash
python -m venv venv
source venv/bin/activate # For Windows: venv\Scripts\activate
```
### Step 3: Install Dependencies
```bash
pip install -r requirements.txt
```
---
## How to Run the System
This system requires **at least two (and optionally three)** separate terminals to run all components.
---
### **Terminal 1: Run the Core API (FastAPI)**
This must be running for other components to work.
```bash
uvicorn api:app --reload --port 8000
```
**What it does:**
Starts the central API hub at [http://127.0.0.1:8000](http://127.0.0.1:8000).
This handles all appointment and doctor data.
---
### **Terminal 2: Run the AI Agent Bridge (MCP)**
This connects the AI agent to the backend API.
```bash
uvicorn server.main:mcp --reload --port 8001
```
**What it does:**
Starts the MCP server at [http://127.0.0.1:8001](http://127.0.0.1:8001).
The AI agent (e.g., Claude or other compatible agents) connects here to access scheduling tools.
---
### **Terminal 3: Run the Dashboard (Optional)**
This visual dashboard helps monitor and manage appointments.
```bash
streamlit run dashboard.py
```
**What it does:**
Launches a dashboard view in your browser for easier interaction and visualization.
---
## 📋 API Endpoints
Once the API server is running, you can interact with it through the Swagger interface at
👉 [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
| Method | Endpoint | Description |
| ---------- | -------------------- | ------------------------------------ |
| **POST** | `/appointments` | Schedule a new appointment |
| **GET** | `/appointments` | Retrieve all scheduled appointments |
| **DELETE** | `/appointments/{id}` | Cancel a specific appointment by ID |
| **GET** | `/doctors` | Retrieve a list of available doctors |
---
## Example Prompts
You can test the system with the following natural language prompts:
* “Book an appointment with Dr. Ramesh Gupta for 11 AM tomorrow.”
* “Cancel my appointment with Dr. Sushma Desai.”
* “Reschedule my appointment with Dr. Sunita Sharma to Friday 3 PM.”
* “Show all my appointments.”
* “List available doctors.”
---
## Technologies Used
* **Python 3.10+**
* **FastAPI**
* **FastMCP Framework**
* **Streamlit (Dashboard)**
* **JSON for Data Storage**
---
