Kusto MCP Server

Overview

The Kusto MCP Server bridges AI assistants with Azure Data Explorer (ADX), allowing developers to query and explore large analytical datasets directly from their conversational tools. By exposing a set of well‑defined MCP tools, the server eliminates the need for custom connectors or manual authentication steps when integrating ADX into AI workflows. This is particularly valuable for teams that rely on real‑time analytics, log inspection, or telemetry analysis and want to harness the power of AI assistants for data discovery and insight generation.

At its core, the server offers a collection of commands that map to common ADX operations:

• Table discovery – List internal tables, external tables, and materialized views so users can understand the data landscape without leaving the chat interface.
• Schema inspection – Retrieve detailed column definitions for both internal and external tables, enabling AI assistants to generate accurate queries or explain data structures.
• Query execution – Run arbitrary Kusto Query Language (KQL) statements against any table or view, returning results in a structured format that can be parsed by downstream tools or displayed directly to the user.

These capabilities are wrapped in a single MCP endpoint, making it straightforward to add to any Claude Desktop or other MCP‑compatible client. Developers simply configure the server in their , providing cluster credentials or a local emulator URL, and the assistant can start issuing Kusto commands with no additional code.

Key Features & Benefits

• Unified authentication – The server handles OAuth token acquisition using Azure AD credentials, so the assistant never needs to manage secrets directly.
• Zero‑code integration – Once configured, the MCP tools are available out of the box; developers can invoke them via natural language prompts without writing custom adapters.
• Extensible tool set – The server’s design allows additional Kusto operations to be added later, such as creating or deleting tables, without changing the client side.
• Local development support – By supporting a local ADX emulator, teams can prototype and test queries offline before deploying to production clusters.

Real‑World Use Cases

• Operational monitoring – An AI assistant can answer questions like “Show me the top 10 error logs from the last hour” by querying a telemetry table in ADX.
• Data exploration – Data scientists can ask the assistant to list available datasets or explain column types, accelerating onboarding and hypothesis generation.
• Incident response – Security teams can quickly run KQL queries against threat intelligence tables and receive concise summaries directly in their chat tool.
• Business analytics – Executives can request trend reports or KPI calculations that are computed on demand by the server, eliminating manual dashboard refreshes.

Unique Advantages

What sets this MCP server apart is its tight coupling to Azure Data Explorer’s native capabilities while remaining completely transparent to the AI assistant. The server abstracts authentication, query execution, and schema discovery behind a simple set of tools, allowing developers to focus on building conversational experiences rather than plumbing. Its compatibility with both cloud clusters and local emulators makes it ideal for continuous integration pipelines, where tests can run against a sandboxed ADX instance before hitting production data.

In summary, the Kusto MCP Server provides a ready‑made, secure, and extensible bridge between AI assistants and Azure Data Explorer, empowering developers to unlock the analytical power of ADX directly within conversational workflows.