Azure/BQ MCP Optimiser
The Azure/BQ MCP Optimiser is a powerful, extensible MCP server designed to provide FinOps and performance insights for both Google BigQuery and Microsoft Azure SQL Server. It features a modular architecture that allows for easy expansion to other data sources and includes an AI-powered tool to translate natural language questions into SQL queries.
Key Features
- Dual Data Source Support: Connect to either Google BigQuery or Azure SQL Server by simply changing an environment variable.
- Live Performance Monitoring: Get real-time insights into your database's performance, including cost analysis for BigQuery and resource usage (CPU, DTU) for Azure SQL.
- Expensive Query Analysis: Identify the most resource-intensive queries in your database to pinpoint optimization opportunities.
- AI-Powered Querying: Translate natural language questions into SQL queries using Azure OpenAI.
- Extensible Architecture: The modular design, built around a
BaseDataSourceclass, makes it easy to add support for other databases in the future. - Robust and Production-Ready: With structured logging and comprehensive error handling, the server is built to be reliable and easy to maintain.
Getting Started
1. Prerequisites
- Python 3.8+
- An active Google Cloud Platform account (for BigQuery) or Microsoft Azure account (for Azure SQL).
2. Installation
Clone the repository and install the required dependencies:
git clone <your-repo-url>
cd <your-repo-name>
pip install -r requirements.txt
3. Configuration
The application is configured using an .env file. A sample .env file is included in the repository. Rename it from .env.sample to .env and fill in the required values.
a. Choose Your Data Source
First, set the DATA_SOURCE_TYPE variable to either "bigquery" or "azuresql".
DATA_SOURCE_TYPE="bigquery"
b. Configure BigQuery
If you are using BigQuery, you will need to provide the following:
GCP_PROJECT_ID: Your Google Cloud project ID.GOOGLE_APPLICATION_CREDENTIALS: The path to your GCP service account key file.
c. Configure Azure SQL
If you are using Azure SQL, you will need to provide the following:
AZURE_SQL_SERVER,AZURE_SQL_DATABASE,AZURE_SQL_USERNAME,AZURE_SQL_PASSWORD: Your Azure SQL database connection details.AZURE_SUBSCRIPTION_ID,AZURE_RESOURCE_GROUP: Your Azure subscription and resource group names.AZURE_TENANT_ID,AZURE_CLIENT_ID,AZURE_CLIENT_SECRET: Your Azure service principal credentials.AZURE_OPENAI_API_KEY,AZURE_OPENAI_ENDPOINT,AZURE_OPENAI_DEPLOYMENT_NAME: Your Azure OpenAI service details.
4. Verify Your Configuration
Before running the server, you can use the verify_env.py script to check that your environment is configured correctly:
python verify_env.py
5. Run the Server
Once your configuration is verified, you can start the MCP server:
python mcp_server.py
Available Tools
The following tools are available through the MCP server:
get_cost_summary: Get a summary of your database's performance and cost.get_expensive_queries: Get a list of the most resource-intensive queries.get_project_costs: Get a breakdown of costs by project (for BigQuery) or database size (for Azure SQL).get_cost_trends: Get historical data on cost or resource usage.analyze_query_cost: Get an estimated execution plan for a query.get_cost_by_user: Get a breakdown of database usage by user.natural_language_to_sql: Translate a natural language question into a SQL query (Azure SQL only).