How to Use the Azure Log Analytics Workspace MCP in Mastra AI

Q: How do I build a workflow in Mastra AI that branches based on Azure Log Analytics Workspace results?

Your agent calls the querylogs tool. Then, you use standard TypeScript or JavaScript in your Mastra workflow to check the result. An if statement on the result count is all you need to trigger different actions.

Q: Can I require human approval before my Mastra AI agent acts on data from the Azure Log Analytics Workspace?

Yes. Mastra AI supports requireToolApproval. You can configure your agent to query the logs, propose an action based on the findings, and then wait for a human to approve it before proceeding.

Build failure-resistant automations that query your Azure logs. Mastra AI adds retries and conditional logic to your monitoring workflows.

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Connect Azure Log Analytics Workspace MCP to Mastra AI

Create your Vinkius account to connect Azure Log Analytics Workspace to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Azure Log Analytics Workspace with Mastra AI

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Automated Log Triage

The `query_logs` tool lets your Mastra AI agent check your Azure Log Analytics Workspace on a schedule. It's a simple, powerful building block for automation. Run a KQL query every five minutes to look for new errors. When the agent finds something, Mastra's workflow engine takes over. You can design it to automatically branch to another step, like paging an on-call engineer or creating a ticket in your project management tool. It turns passive log data into action.

Add Logic to Log Monitoring

Go beyond simple alerts. With Mastra AI, your agent can execute a `query_logs` call, analyze the number of results, and decide what to do next. This is observability with conditional logic. For example: zero results? Do nothing. One to five results? Send a low-priority notification. More than five? Escalate immediately to a human. You define the thresholds and the actions, building a smarter response system.

A Resilient Mastra AI MCP Server

This MCP server gives your agent a direct, reliable line to your logs. Mastra AI's built-in features like automatic retries with exponential backoff mean that temporary network blips won't break your workflow. If a call to `query_logs` fails, Mastra can try again automatically. This makes your monitoring automations more robust. You can build workflows that you trust to run without constant supervision.

Setup guide

Set up Azure Log Analytics Workspace MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Azure Log Analytics Workspace tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "azure-log-analytics-workspace-mcp-client",
  servers: {
    "azure-log-analytics-workspace-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Azure Log Analytics Workspace Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Azure Log Analytics Workspace tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Azure Log Analytics Workspace transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Azure Log Analytics Workspace. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Azure Log Analytics Workspace MCP in Mastra AI

Yes. Mastra AI has built-in support for exponential backoff and retries. You can configure your agent to automatically re-run the `query_logs` tool if it fails due to a transient issue.

Your agent calls the `query_logs` tool. Then, you use standard TypeScript or JavaScript in your Mastra workflow to check the result. An `if` statement on the result count is all you need to trigger different actions.

Yes. Mastra AI supports `requireToolApproval`. You can configure your agent to query the logs, propose an action based on the findings, and then wait for a human to approve it before proceeding.

No. The Mastra AI client automatically detects the best transport method. You just provide the server URL, and it handles the connection details for you.

You don't manage them. The Vinkius platform handles the secure connection to Azure. Your Mastra AI agent uses a single endpoint token to call the MCP server, which then uses its pre-authorized connection to query your log data. Your credentials are never exposed to the agent.

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