How to Use the Honeycomb MCP in Mastra AI

Q: Can Mastra AI agents automatically update Honeycomb dashboards?

While you can read dashboards using listhoneycombboards, modifying them requires direct API access. Your agent can, however, generate new query specs using createqueryspecification and output the links for your team.

Q: How do I register the Honeycomb MCP Server with a Mastra AI agent?

You initialize the client using the @mastra/mcp package and point it to your Vinkius server URL. After that, you call listTools and spread the returned tools directly into your agent's configuration object.

Automate Honeycomb incident response workflows with Mastra AI agents using native human-in-the-loop approvals.

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Setup Honeycomb with Mastra AI

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Automated Incident Triage with Mastra AI

This Honeycomb MCP Server exposes `list_triggers` so your Mastra AI agent can monitor active telemetry alerts in real-time. When an alert fires, the workflow engine triggers conditional branching to investigate the root cause. The agent calls `create_query_specification` to build a diagnostic query based on the alert payload. It then runs the query and parses the results without requiring any manual intervention from your on-call engineer.

Human-in-the-Loop Marker Creation

The `create_marker` tool allows Mastra agents to annotate dataset timelines using this MCP Server during production incidents. SREs can set `requireToolApproval` on this action to prevent the agent from writing markers without a sanity check. Once an engineer approves the Slack notification, the agent executes the call to flag the incident start. This gives you absolute control over your Honeycomb timeline annotations during high-pressure outages.

Multi-Step Board Analysis

The `list_honeycomb_boards` tool lets Mastra workflows pull down shared team dashboards during an active outage. Analyzing these board definitions helps the agent locate relevant query IDs for deeper inspection. By calling `get_query_result` sequentially, the agent compiles a diagnostic report of all failing services. The workflow then deploys this summary to your incident channel using Mastra's built-in deployment engine.

Setup guide

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

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

const result = await agent.generate(
  "List recent Honeycomb 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 Honeycomb. 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 Honeycomb MCP in Mastra AI

You can configure Mastra's workflow engine with exponential backoff retries for the `get_query_result` tool. This ensures that if Honeycomb takes a few seconds to process a massive dataset, your agent won't crash.

While you can read dashboards using `list_honeycomb_boards`, modifying them requires direct API access. Your agent can, however, generate new query specs using `create_query_specification` and output the links for your team.

You initialize the client using the `@mastra/mcp` package and point it to your Vinkius server URL. After that, you call `listTools` and spread the returned tools directly into your agent's configuration object.

Yes, Mastra's local development server supports the SSE transport out of the box. You can test your incident response loops locally before deploying the agent to your cloud environment.

Your query specifications and dataset slugs are processed entirely in memory within the V8 MCP sandbox. No database stores these telemetry parameters, ensuring your system architecture remains completely private and isolated.

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