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Mcp Server Kusto

@ZZZHDW

A mcp server that uses azure data explorer as a backend

Overview

What is Mcp Server Kusto?

Mcp Server Kusto is an MCP server that provides access to Azure Data Explorer (ADX) clusters. It enables AI assistants to list tables, execute queries, and retrieve table schemas from ADX.

How to use Mcp Server Kusto?

Install and configure by editing claude_desktop_config.json with the uv command and arguments pointing to the project directory and cluster URL. For Azure AD authentication, provide --authority_id, --client_id, and --client_secret. For a local emulator, omit those flags and use a URL like https://localhost:8082.

Key features of Mcp Server Kusto

  • List internal tables, external tables, and materialized views
  • Execute queries on internal and external tables
  • Get schema of internal tables, external tables, and materialized views
  • Supports Azure AD authentication (tenant, client ID, secret)
  • Supports local Azure Data Explorer emulator without authentication

Use cases of Mcp Server Kusto

  • Query ADX clusters directly from Claude Desktop conversations
  • Explore available tables and views in a cluster before writing queries
  • Retrieve table schemas to understand data structures for query formulation
  • Run ad-hoc analytics on ADX data through a natural language interface

FAQ from Mcp Server Kusto

How do I configure authentication for Mcp Server Kusto?

Provide the --authority_id (tenant ID), --client_id, and --client_secret arguments in the Claude Desktop configuration.

Can I use Mcp Server Kusto with a local ADX emulator?

Yes. Use a cluster URL like https://localhost:8082 and omit the --authority_id, --client_id, and --client_secret arguments.

What tools are available in Mcp Server Kusto?

The server provides tools to list internal/external tables and materialized views, execute queries on internal/external tables, and get schemas of internal/external tables and materialized views.

What are the runtime dependencies for Mcp Server Kusto?

The server requires uv to run, and the Python project located at the specified directory path must be available.

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