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🚀 MCP KQL Server

@4R9UN

Repository

About 🚀 MCP KQL Server

Kusto and Log Analytics MCP server help you execute a KQL (Kusto Query Language) query within an AI prompt, analyze, and visualize the data.

Basic information

Category

Data & Analytics

License

MIT

Runtime

python

Transports

stdio

Publisher

4R9UN

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-kql-server": {
      "command": "python",
      "args": [
        "-m",
        "mcp_kql_server",
        "--transport",
        "http",
        "--host",
        "127.0.0.1",
        "--port",
        "8000",
        "--http-path",
        "/mcp",
        "--stateless-http"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP KQL Server?

MCP KQL Server is an MCP server that transforms natural language questions into optimized KQL queries with intelligent schema discovery, AI-powered caching, and seamless Azure Data Explorer integration. It is for data analysts, developers, and AI agents who need to query Azure Data Explorer using plain English.

How to use MCP KQL Server?

Install with pip install mcp-kql-server, then configure your MCP client (e.g., Claude Desktop, VSCode) to launch the server via python -m mcp_kql_server. The server provides two main tools: execute_kql_query for NL2KQL conversion and direct KQL execution, and kql_schema_memory for schema discovery and cache management.

Key features of MCP KQL Server

  • Natural Language to KQL (NL2KQL) conversion
  • Execute raw KQL queries directly
  • Schema discovery and AI-powered caching
  • Support for JSON, CSV, and table output formats
  • Schema-grounded repair of invalid query columns
  • Cache management and memory statistics

Use cases of MCP KQL Server

  • Query Azure Data Explorer without writing KQL manually
  • Explore database schemas and table structures interactively
  • Generate analysis reports with visualizations from natural language prompts

FAQ from MCP KQL Server

What are the prerequisites for running MCP KQL Server?

Python 3.10 or higher, Azure CLI installed and authenticated (via az login), and access to an Azure Data Explorer cluster.

How do I configure MCP KQL Server for my MCP client?

Add a stdio or HTTP entry to your client's MCP settings file. For Claude Desktop, add {"command": "python", "args": ["-m", "mcp_kql_server"]} to mcp_settings.json. For shared environments, use --transport http.

What transports does MCP KQL Server support?

It supports stdio, HTTP, streamable HTTP, and SSE transports, configurable via the --transport option or the FASTMCP_TRANSPORT environment variable.

How does the server handle authentication?

Authentication is delegated to the Azure CLI. The server can check Azure CLI auth on startup via --auth-on-startup or the MCP_KQL_AUTH_ON_STARTUP environment variable.

Where does MCP KQL Server store its schema cache?

Cached schema data is stored in %APPDATA%\KQL_MCP on Windows or ~/.local/share/KQL_MCP on Linux and macOS.

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