Azure Data Explorer MCP Server

The Azure Data Explorer (ADX) MCP server bridges the gap between powerful cloud analytics and conversational AI. By exposing a standardized Model Context Protocol interface, it allows assistants such as Claude to query ADX clusters directly, turning raw data into actionable insights without leaving the chat. This solves a common pain point for developers and analysts: integrating complex Kusto Query Language (KQL) operations into AI‑driven workflows while maintaining security, scalability, and ease of use.

At its core, the server offers a set of intuitive tools that let an AI client execute KQL queries and explore database resources. Users can list tables, view schemas, sample data, and retrieve table statistics—all through a single, well‑defined API surface. Authentication is handled automatically using Azure credentials: the server prefers Workload Identity for Kubernetes workloads, falling back to default Azure credential chains when necessary. This means that once the environment is configured, developers can focus on crafting queries rather than managing tokens or secrets.

Key capabilities include:

• KQL Execution – Run any valid query against the configured cluster and database, returning structured results that can be fed back into AI reasoning or displayed to end users.
• Resource Discovery – Dynamically enumerate tables, inspect schemas, and fetch sample rows to aid in query construction or data validation.
• Custom Tool Selection – The tool list is configurable, allowing teams to expose only the functionality they need and keep context windows lean.
• Docker & Transport Flexibility – Deploy as a container or use HTTP, SSE, or stdio transports to fit into existing infrastructure.

Real‑world scenarios abound. A data scientist can ask an AI assistant to “show me the top 10 error rates from last month” and receive a ready‑to‑use KQL query, while an operations engineer can prompt the assistant to “generate a dashboard of latency trends” and get a sample query that feeds into Power BI. In automated pipelines, the MCP server can act as an intermediary between scheduled jobs and AI‑driven anomaly detection scripts.

Because it follows the MCP specification, the server integrates seamlessly with any AI client that understands the protocol. This means developers can plug it into Claude, Gemini, or future assistants with minimal friction, adding real‑time analytics to conversations, reports, and decision support systems. The combination of Azure’s secure authentication, ADX’s high‑performance analytics, and MCP’s standardized interface makes this server a standout solution for embedding data intelligence into AI workflows.