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Azure Log Analytics MCP Server

Azure Log Analytics MCP Server

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github.com/MananShahTRData & Analytics3Updated 84w ago
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About Azure Log Analytics MCP Server

An MCP server and CLI for querying Azure Log Analytics, aimed at teams investigating logs with natural-language requests. It uses Claude AI to turn queries into KQL and returns formatted results.

Key features

  1. 01Natural language to KQL conversion
  2. 02Azure Log Analytics query execution
  3. 03LLM-ready result formatting
  4. 04CLI and MCP server modes

Use cases

  1. 01Find authentication errors in a time range
  2. 02Run KQL queries from natural-language requests

MCP support

Exposes a `query_logs` tool that accepts a natural-language query, optional time range, and result limit, then queries Azure Log Analytics.

#azure-log-analytics#mcp-server#natural-language-query

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Azure Log Analytics MCP Server

An MCP (Model Context Protocol) server for querying Azure Log Analytics using natural language. This server allows large language models to convert natural language queries into KQL (Kusto Query Language) and execute them against Azure Log Analytics.

Features

  • Convert natural language queries to KQL using Claude AI
  • Execute KQL queries against Azure Log Analytics
  • Format results for easy consumption by LLMs
  • CLI mode for direct interactions and MCP server mode for LLM integrations

Prerequisites

  • Node.js 18.x or higher
  • An Azure subscription with Log Analytics workspace
  • An Anthropic API key for Claude AI
  • Azure CLI configured with appropriate credentials

Installation

# Clone the repository
git clone https://github.com/MananShahTR/azure-log-analytics-mcp.git
cd azure-log-analytics-mcp

# Install dependencies
npm install

# Build the project
npm run build

Configuration

The server requires the following environment variables:

  • ANTHROPIC_API_KEY: Your Anthropic API key for Claude AI

Azure credentials are obtained through Azure CLI credentials. Ensure you're logged in with az login before running the server.

You'll need to configure the following in the azure-service.ts file:

  • subscriptionId: Your Azure subscription ID
  • resourceGroup: The resource group containing your App Insights resource
  • appInsightsId: The name of your Application Insights resource

Usage

CLI Tool
# Run as a CLI tool
ANTHROPIC_API_KEY=your_key_here node build/index.js
MCP Server
# Run as an MCP server
ANTHROPIC_API_KEY=your_key_here node build/mcp-server.js
MCP Settings Configuration

Add the following to your MCP settings configuration file:

{
  "mcpServers": {
    "azure-log-analytics": {
      "command": "node",
      "args": ["path/to/azure-log-analytics-mcp/build/mcp-server.js"],
      "env": {
        "ANTHROPIC_API_KEY": "your_key_here"
      }
    }
  }
}

Tool Usage

Once connected, the MCP server provides the following tool:

  • query_logs: Query Azure Log Analytics using natural language
    • Parameters:
      • query: Natural language query about trace logs (required)
      • timeRange: Optional time range (e.g., "last 24 hours", "past week")
      • limit: Maximum number of results to return

Examples

// Example MCP tool use
use_mcp_tool({
  server_name: "azure-log-analytics",
  tool_name: "query_logs",
  arguments: {
    query: "Show me all errors in the authentication service from the last hour",
    timeRange: "last hour",
    limit: 10
  }
});

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.