Kusto MCP Server

Overview

The Mcp Server Kusto is an MCP (Model Context Protocol) server that bridges AI assistants with Azure Data Explorer (ADX), also known as Kusto. By exposing a set of tools that interact directly with ADX clusters, it allows conversational agents to query, inspect, and explore large datasets without leaving the chat interface. This capability is especially valuable for developers who need to surface real‑time analytics, audit logs, or telemetry data in natural language workflows.

What Problem It Solves

Many AI assistants are limited to static knowledge bases or external APIs that return JSON. When dealing with time‑series logs, telemetry streams, or large analytical datasets stored in ADX, developers must normally write custom code, run queries manually, and then format results for the user. The Kusto MCP server eliminates this friction by providing a ready‑made, authenticated connection to ADX clusters. It handles authentication (OAuth via tenant/client credentials) and exposes high‑level operations that can be invoked directly from the assistant, streamlining data exploration and reducing boilerplate.

Core Functionality

The server offers a focused set of tools that map to common ADX operations:

• Listing tables and views: Retrieve internal, external, or materialized view names in a cluster.
• Query execution: Run Kusto Query Language (KQL) against internal or external tables, returning results in a structured format.
• Schema discovery: Fetch column definitions and types for both internal and external tables, enabling assistants to present schema summaries or validate user queries.

These tools are designed for rapid iteration; the assistant can first list available tables, then fetch a schema, and finally run a query—all through simple tool calls. The server also supports local ADX emulators, simplifying development and testing without needing a live cluster.

Use Cases

• Real‑time monitoring: An assistant can pull the latest metrics from a telemetry table and display them in conversation, enabling on‑call engineers to get quick insights.
• Data discovery: Data scientists can ask the assistant about available datasets, their schemas, and sample queries without leaving their IDE or chat window.
• Audit and compliance: Security teams can query audit logs stored in ADX directly through the assistant, facilitating rapid investigations.
• Operational support: IT staff can ask for recent incidents or performance metrics, and the assistant will execute KQL queries against internal tables to provide answers.

Integration with AI Workflows

Because MCP servers are first‑class citizens in the Claude ecosystem, developers can add the Kusto server to their configuration file and reference its tools in prompts. The assistant automatically handles authentication tokens, query execution, and result formatting, allowing developers to focus on higher‑level conversational logic. This tight integration means that complex analytical tasks can be performed on demand, and the results are returned in a conversationally friendly format.

Unique Advantages

• Native ADX support: Unlike generic SQL connectors, the server understands Kusto’s data model and query language, providing accurate schema information and efficient execution.
• Authentication abstraction: OAuth credentials are managed by the server, freeing developers from handling secrets in prompt logic.
• Local emulator compatibility: Development and testing can occur against a local ADX instance, accelerating iteration cycles.
• Minimal surface area: The focused set of tools reduces cognitive load for developers, making it easy to adopt without a steep learning curve.

In summary, the Mcp Server Kusto equips AI assistants with direct, authenticated access to Azure Data Explorer, enabling developers to build conversational analytics applications that are both powerful and developer‑friendly.