Kusto MCP Server
by abhirockzz
This is an MCP server implementation for Azure Data Explorer (Kusto), built using the Go SDK. It enables easier data analysis and exploration within MCP-compatible tools like VS Code.
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What is Kusto MCP Server?
This MCP server provides tools for interacting with Azure Data Explorer (Kusto), allowing users to list databases and tables, get table schemas, and execute read-only KQL queries.
How to use Kusto MCP Server?
- Clone the repository. 2. Build the server using
go build -o mcp_kusto main.go. 3. Configure the server in your MCP client (e.g., VS Code, Claude Desktop) using a configuration file (mcp.json). 4. Ensure the user principal has necessary permissions in Azure Data Explorer. 5. Authenticate using DefaultAzureCredential (e.g., Azure CLI login).
Key features of Kusto MCP Server
Lists databases in a Kusto cluster
Lists tables in a Kusto database
Retrieves table schemas
Executes read-only KQL queries
Use cases of Kusto MCP Server
Exploring data in Azure Data Explorer using VS Code
Analyzing data in Azure Data Explorer using Claude Desktop
Automating data discovery tasks
Integrating Kusto with other MCP-compatible tools
FAQ from Kusto MCP Server
What is an MCP server?
What is an MCP server?
MCP (Model Context Protocol) is a protocol that allows tools like VS Code to interact with data sources and other services.
What is Azure Data Explorer (Kusto)?
What is Azure Data Explorer (Kusto)?
Azure Data Explorer is a fast, fully managed data analytics service for real-time analysis of large volumes of data streaming from applications, websites, IoT devices, and more.
What permissions are required to use this server?
What permissions are required to use this server?
The user principal needs permissions to list databases and tables, get table schemas, and execute queries on the database. Refer to the Azure Data Explorer documentation for details.
How does authentication work?
How does authentication work?
The server uses DefaultAzureCredential, which looks for credentials in environment variables or locally installed developer tools like the Azure CLI.
Why should I validate the results and queries?
Why should I validate the results and queries?
Large Language Models (LLMs) are non-deterministic and can make mistakes. Always validate the results and queries before making any decisions based on them.