How to Use the Datadog MCP in Mastra AI

Q: Can a Mastra AI agent mute Datadog monitors conditionally?

Absolutely. You can build a workflow where the agent calls listmonitors, evaluates their status, and then decides whether to run mutemonitor based on your custom branching rules.

Run self-healing workflows that query Datadog logs and mute monitors automatically using Mastra AI agents.

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Connect Datadog MCP to Mastra AI

Create your Vinkius account to connect Datadog 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 Datadog with Mastra AI

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Trigger Mastra AI workflows from Datadog incidents

When a Datadog alert fires, you can run a Mastra AI workflow that calls `get_incident` and `list_hosts` to diagnose the root cause before a human even wakes up. If the workflow detects a known memory leak, it can execute custom branching logic to restart the service and verify recovery. No manual intervention required.

Manage Datadog monitors with Mastra AI retries

This MCP Server lets your Mastra agent call `mute_monitor` or `search_monitors` with built-in exponential backoff. If Datadog rate limits your request, Mastra automatically retries the operation until the monitor is safely muted. You get reliable operations without writing nested try-catch blocks.

Fetch Datadog SLOs inside Mastra AI workflows

Keep your engineering team honest by tracking service level objectives automatically using `list_slos` over our MCP Server to gather performance data on a schedule. The workflow engine parses the SLO metrics, checks them against your targets, and triggers alerts if your error budget is running low.

Setup guide

Set up Datadog 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 Datadog 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: "datadog-extended-mcp-client",
  servers: {
    "datadog-extended-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Datadog transactions"
);
console.log(result.text);

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Common questions about Datadog MCP in Mastra AI

You register this MCP Server in your Mastra config and expose `get_incident` to your workflow steps. The workflow engine then passes the incident JSON to your agents for automated analysis.

Absolutely. You can build a workflow where the agent calls `list_monitors`, evaluates their status, and then decides whether to run `mute_monitor` based on your custom branching rules.

Mastra's built-in retry engine handles rate limits gracefully. When `query_metrics` hits a limit, the workflow backs off and retries, ensuring your automated reports never fail midway.

No, Mastra AI auto-detects the SSE or HTTP transport provided by Vinkius. You just pass the MCP endpoint URL to the client constructor and start calling tools.

Yes, your hostnames and metric definitions never leave your execution environment. Vinkius processes all `list_hosts` and metric queries inside sandboxed isolates, keeping your metadata secure on this MCP Server.

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