LLM + REST API + MySQL + FastMCP Learning Project

This project is a hands-on implementation to understand:

• LLM API integration from scratch (Gemini)
• REST API development using FastAPI
• MySQL CRUD operations
• Tool calling architecture
• FastMCP for reducing LLM boilerplate code

Each concept is implemented as an independent method for step-by-step learning.

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📂 Project Structure

Gemini-NLP-to-MySQL-via-FastMCP-main

├── Method_1_Calling_Func_Same_File/
│   └── function_call_1.py
│
├── Method_2_Calling_Func_From_Different_File/
│   └── other_file_call_2.py
│
├── Method_3_Using_RestAPI_to_CRUD_Data/
│   └── fastapi_rest_api_method_call_3.py
│
├── Method_4_Using_Gemini_LLM_API/
│   ├── sql_database_tools_funcs.py
│   ├── gemini_api_call_4.py
│   ├── gemini_llm_just_chat.py
│   └── llm_tools_mapper.py
│
├── Method_5_Using_FastMCP_Gemini_LLM_API/
│   ├── db_tools_mcp.py
│   ├── gemini_mcp_client_5.py
│   └── mcp_db_server.py
│
├── .env.example
├── .gitignore
├── requirements.txt
└── README.md

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⚙️ Setup Instructions

1️⃣ Clone the repository

git clone https://github.com/dhaRaniv12/Gemini-NLP-to-MySQL-via-FastMCP.git
cd Gemini-NLP-to-MySQL-via-FastMCP-main

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2️⃣ Create virtual environment (recommended)

python -m venv venv
source venv/bin/activate      # Mac/Linux
venv\Scripts\activate         # Windows

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3️⃣ Install dependencies

pip install -r requirements.txt

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4️⃣ Configure environment variables

Create a .env file based on .env.example:

API_KEY='YOUR_GEMINI_API_KEY'
SQL_PW = "YOUR_MySQL_PW"

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🎯 Learning Flow

Method 1 -- Function Call (Same File)

Goal: - Understand basic Python function execution - Control flow inside a single script

Run:

python Method_1_Calling_Func_Same_File/function_call_1.py

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Method 2 -- Function Call (Different File)

Goal: - Learn modular programming - Use imports and separation of logic across files

Run:

python Method_2_Calling_Func_From_Different_File/other_file_call_2.py

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Method 3 -- FastAPI REST API + MySQL (CRUD)

Goal: - Build REST endpoints - Perform Create, Read, Update, Delete operations on MySQL - Test APIs using Swagger UI

Run server:

fastapi dev Method_3_Using_RestAPI_to_CRUD_Data/fastapi_rest_api_method_call_3.py

Open Swagger docs:

http://127.0.0.1:8000/docs

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Method 4 -- Gemini LLM API (NLP → DB Operations)

Features:

• Accept natural language input
• Convert NLP → structured database operation
• Execute MySQL queries via db_tools.py
• Tool execution handled by tool_executor.py
• gemini_llm_just_chat.py for basic chat testing

Run:

python Method_4_Using_Gemini_LLM_API/gemini_api_call_4.py

Example input:

Add a task at tomorrow 3pm to order groceries

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Method 5 -- FastMCP + Gemini

Goal:

• Eliminate redundant tool-calling logic from Method 4
• Use FastMCP for standardized tool execution
• Improve maintainability and architecture

Run:

python Method_5_Using_FastMCP_Gemini_LLM_API/gemini_mcp_client_5.py

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🧱 Tech Stack

• Python
• FastAPI
• MySQL
• google-genai (Gemini API)
• FastMCP

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👨‍💻 Purpose

This is a learning-focused backend + LLM integration project designed to understand real-world architecture step by step, from basic Python function calls to LLM-driven database operations.