QueryBridge

QueryBridge is a local AI application that connects a MySQL database to an Ollama-powered language model through an MCP (Model Context Protocol) tool server. It lets you ask natural-language questions about your data while keeping database access structured, safe, and local.

What this project does

• Exposes MySQL read operations through an MCP server

• Uses a local Ollama LLM to generate a safe SQL query

• Validates the SQL against the real table schema

• Executes the query through the MCP tool layer

• Passes the result back to the LLM for a human-readable answer

This app is intended for local development and experimentation with:

• MySQL running in XAMPP

• Ollama models running on the same machine

• Python 3.12 + uv for project management

Related MCP server: MySQL MCP Server

Architecture

• main.py – client entry point, asks a question and orchestrates the flow

• mcp_server.py – MCP server that exposes safe database tools

• db/connection.py – MySQL connection and SQL execution helpers

• agent.py – Ollama LLM and prompt logic

• .env – local environment variables for DB and model configuration

Prerequisites

Before running the application, make sure you have:

• Python 3.12+

• uv installed

• Ollama installed and running locally

• XAMPP or another local MySQL service running

• A MySQL database named mcp_db

• A table named employee_salary in that database

Install uv

If you do not have uv installed yet:

pip install uv

Or follow the official uv installation instructions for your OS.

Ollama setup

Make sure Ollama is installed and the service is running.

Pull the required models:

ollama pull llama3.1
ollama pull nomic-embed-text

Start Ollama:

ollama serve

If Ollama is already running in the background, you can skip this step.

The app uses these default values:

OLLAMA_BASE_URL=http://localhost:11434
LLM_MODEL=llama3.1
EMBEDDING_MODEL=nomic-embed-text

MySQL / XAMPP setup

This project is configured to work with a local MySQL instance, typically running under XAMPP.

Recommended DB values

Use the following values in your .env file:

DB_HOST=127.0.0.1
DB_NAME=mcp_db
DB_USER=root
DB_PASSWORD=root
DB_PORT=3306

Important notes:

• Use 127.0.0.1 instead of localhost when running MySQL via XAMPP for better reliability on Windows

• Ensure XAMPP MySQL is running before starting the app

• The app expects the database mcp_db to exist

Example table

The application is designed around a table like:

CREATE TABLE employee_salary (
    emp_id INT PRIMARY KEY,
    emp_name VARCHAR(100),
    department VARCHAR(50),
    designation VARCHAR(50),
    base_salary DECIMAL(10,2),
    bonus DECIMAL(10,2),
    total_salary DECIMAL(10,2),
    joining_date DATE
);

Sample rows may look like:

INSERT INTO employee_salary (emp_id, emp_name, department, designation, base_salary, bonus, total_salary, joining_date)
VALUES
(1, 'Alice Johnson', 'Engineering', 'Software Developer', 75000.00, 5000.00, 80000.00, '2021-03-15'),
(2, 'Bob Smith', 'Engineering', 'DevOps Engineer', 72000.00, 4500.00, 76500.00, '2020-07-10');

Environment configuration

Create a .env file in the project root if it does not already exist.

Example:

DB_HOST=127.0.0.1
DB_NAME=mcp_db
DB_USER=root
DB_PASSWORD=root
DB_PORT=3306
OLLAMA_BASE_URL=http://localhost:11434
LLM_MODEL=llama3.1
EMBEDDING_MODEL=nomic-embed-text
TABLE_NAME=employee_salary

You can copy the values from .env.example if present in the project.

Running the project with uv

From the project folder:

cd C:\Users\srb3365\Documents\Projects\Python\querybridge

Install dependencies:

uv sync

Run the app:

uv run python main.py

When prompted, ask a question such as:

what is base salary of Bob Smith

The app will:

1. read the schema from MySQL

2. ask Ollama to generate a safe SELECT query

3. call the MCP database tool

4. return a natural-language answer based on the results

Quick validation commands

Check that MySQL is reachable:

uv run python -c "from db.connection import get_engine; from sqlalchemy import text; e=get_engine(); c=e.connect(); print(c.execute(text('SELECT 1 AS ok')).scalar_one()); c.close()"

Check the table exists:

uv run python -c "from db.connection import get_engine; from sqlalchemy import text; e=get_engine(); c=e.connect(); print(c.execute(text('SHOW TABLES')).fetchall()); c.close()"

Troubleshooting

MySQL connection refused

• Confirm XAMPP MySQL service is running

• Check that port 3306 is enabled

• Use 127.0.0.1 instead of localhost

• Validate the database credentials in .env

Ollama connection failed

• Start Ollama with ollama serve

• Pull the models: ollama pull llama3.1 and ollama pull nomic-embed-text

• Confirm OLLAMA_BASE_URL points to the correct host and port

SQL generated is wrong

This app validates SQL against the current schema and rejects invalid table names or columns. If the model hallucinates a wrong table, the app will use a safe fallback for common employee salary questions.

Notes

This is a local-first project designed to demonstrate:

• local model inference with Ollama

• structured database access through MCP

• MySQL read-only query execution

• AI + tools pattern for business data questions

License

This project is provided for local learning and experimentation.