
Azure Log Analytics MCP Server
MCP server for querying Azure Log Analytics using natural language
what is Azure Log Analytics MCP Server?
Azure Log Analytics MCP Server is a Model Context Protocol server designed to query Azure Log Analytics using natural language, allowing users to convert their queries into Kusto Query Language (KQL) and execute them seamlessly.
how to use Azure Log Analytics MCP Server?
To use the server, clone the repository, install the dependencies, and run it either as a CLI tool or an MCP server by providing your Anthropic API key and Azure credentials.
key features of Azure Log Analytics MCP Server?
- Converts natural language queries to KQL using Claude AI.
- Executes KQL queries against Azure Log Analytics.
- Formats results for easy consumption by large language models (LLMs).
- Supports both CLI mode and MCP server mode for integrations.
use cases of Azure Log Analytics MCP Server?
- Querying Azure Log Analytics for specific log data using natural language.
- Integrating with LLMs for enhanced data analysis and reporting.
- Simplifying log queries for users unfamiliar with KQL.
FAQ from Azure Log Analytics MCP Server?
- What are the prerequisites for using the server?
You need Node.js 18.x or higher, an Azure subscription with a Log Analytics workspace, an Anthropic API key for Claude AI, and Azure CLI configured with appropriate credentials.
- Is there a specific way to run the server?
Yes, you can run it as a CLI tool or as an MCP server by using the provided commands in the documentation.
- Can I customize the queries?
Yes, you can customize your natural language queries to retrieve specific log data as needed.
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 IDresourceGroup
: The resource group containing your App Insights resourceappInsightsId
: 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
- Parameters:
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