# Cloud SQL for SQL Server MCP Server The Cloud SQL for SQL Server Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Google Cloud SQL for SQL Server databases. It supports connecting to instances, exploring schemas, and running queries. ## Features An editor configured to use the Cloud SQL for SQL Server MCP server can use its AI capabilities to help you: - **Query Data** - Execute SQL queries - **Explore Schema** - List tables and view schema details ## Installation and Setup ### Prerequisites * Download and install [MCP Toolbox](https://github.com/googleapis/genai-toolbox): 1. **Download the Toolbox binary**: Download the latest binary for your operating system and architecture from the storage bucket. Check the [releases page](https://github.com/googleapis/genai-toolbox/releases) for OS and CPU architecture support: `https://storage.googleapis.com/genai-toolbox/v0.21.0///toolbox` * Replace `` with `linux`, `darwin` (macOS), or `windows`. * Replace `` with `amd64` (Intel) or `arm64` (Apple Silicon). ``` curl -L -o toolbox https://storage.googleapis.com/genai-toolbox/v0.21.0/linux/amd64/toolbox ``` 2. **Make it executable**: ```bash chmod +x toolbox ``` 3. **Move binary to `/usr/local/bin/` or `/usr/bin/`**: ```bash sudo mv toolbox /usr/local/bin/ # sudo mv toolbox /usr/bin/ ``` **On Windows, move binary to the `WindowsApps\` folder**: ``` move "C:\Users\\toolbox.exe" "C:\Users\\AppData\Local\Microsoft\WindowsApps\" ``` **Tip:** Ensure the destination folder for your binary is included in your system's PATH environment variable. To check `PATH`, use `echo $PATH` (or `echo %PATH%` on Windows). **Note:** You may need to restart Antigravity for changes to take effect. * A Google Cloud project with the **Cloud SQL Admin API** enabled. * Ensure [Application Default Credentials](https://cloud.google.com/docs/authentication/gcloud) are available in your environment. * IAM Permissions: * Cloud SQL Client (`roles/cloudsql.client`) ### Configuration The MCP server is configured using environment variables. ```bash export CLOUD_SQL_MSSQL_PROJECT="" export CLOUD_SQL_MSSQL_REGION="" export CLOUD_SQL_MSSQL_INSTANCE="" export CLOUD_SQL_MSSQL_DATABASE="" export CLOUD_SQL_MSSQL_USER="" # Optional export CLOUD_SQL_MSSQL_PASSWORD="" # Optional export CLOUD_SQL_MSSQL_IP_TYPE="PUBLIC" # Optional: `PUBLIC`, `PRIVATE`, `PSC`. Defaults to `PUBLIC`. ``` > **Note:** If your instance uses private IPs, you must run the MCP server in the same Virtual Private Cloud (VPC) network. Add the following configuration to your MCP client (e.g., `settings.json` for Gemini CLI): ```json { "mcpServers": { "cloud-sql-mssql": { "command": "toolbox", "args": ["--prebuilt", "cloud-sql-mssql", "--stdio"], "env": { "CLOUD_SQL_MSSQL_PROJECT": "your-project-id", "CLOUD_SQL_MSSQL_REGION": "your-region", "CLOUD_SQL_MSSQL_INSTANCE": "your-instance-id", "CLOUD_SQL_MSSQL_DATABASE": "your-database-name", "CLOUD_SQL_MSSQL_USER": "your-username", "CLOUD_SQL_MSSQL_PASSWORD": "your-password" } } } } ``` ## Usage Once configured, the MCP server will automatically provide Cloud SQL for SQL Server capabilities to your AI assistant. You can: * "Select top 10 rows from the customers table." * "List all tables in the database." ## Server Capabilities The Cloud SQL for SQL Server MCP server provides the following tools: | Tool Name | Description | |:--------------|:-----------------------------------------------------------| | `execute_sql` | Use this tool to execute SQL. | | `list_tables` | Lists detailed schema information for user-created tables. | ## Documentation For more information, visit the [Cloud SQL for SQL Server documentation](https://cloud.google.com/sql/docs/sqlserver).