What is Azure Log Analytics Mcp

azure-log-analytics-mcp is an MCP server designed for querying Azure Log Analytics using natural language. It enables large language models to translate natural language queries into Kusto Query Language (KQL) and execute them against Azure Log Analytics.

Use cases

Use cases include querying logs for specific errors, analyzing performance metrics over a defined time range, and generating reports based on natural language requests, making it easier for non-technical users to interact with log data.

How to use

To use azure-log-analytics-mcp, you need to install the server by cloning the repository, installing dependencies, and building the project. You can run it either as a CLI tool or as an MCP server. Configuration requires setting environment variables and defining Azure credentials.

Key features

Key features include converting natural language queries to KQL using Claude AI, executing KQL queries against Azure Log Analytics, formatting results for easy consumption by LLMs, and providing both CLI mode and MCP server mode for integration.

Where to use

azure-log-analytics-mcp can be used in various fields such as IT operations, application monitoring, and data analysis, where querying logs and analytics data in natural language can enhance user experience and accessibility.

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