How to integrate Honeycomb MCP with Mastra AI

Connect Mastra AI to Honeycomb MCP. Investigate latency spikes in production dataset, compare error rates by endpoint today, and more using natural language, with authentication handled for you.

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Honeycomb is an observability platform for high-cardinality event data. It helps teams debug production systems fast with rich telemetry analysis.

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Introduction

This guide walks you through connecting Honeycomb to Mastra AI using the Composio tool router. By the end, you'll have a working Honeycomb agent that can investigate latency spikes in production dataset, compare error rates by endpoint today, find anomalous traces in checkout service through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Honeycomb account through Composio's Honeycomb MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Honeycomb tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Honeycomb tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Honeycomb agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Honeycomb MCP server, and what's possible with it?

The Honeycomb MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeycomb account. It provides structured and secure access so your agent can perform Honeycomb operations on your behalf.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Honeycomb through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Honeycomb

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["honeycomb_mcp"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Honeycomb MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "honeycomb_mcp" for Honeycomb access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Honeycomb toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "honeycomb_mcp-mastra-agent",
    instructions: "You are an AI agent with Honeycomb tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        honeycomb_mcp: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Honeycomb toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Honeycomb MCP and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["honeycomb_mcp"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      honeycomb_mcp: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "honeycomb_mcp-mastra-agent",
    instructions: "You are an AI agent with Honeycomb tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { honeycomb_mcp: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Honeycomb through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
TOOLS

Supported Tools

Every Honeycomb MCP action and event your agent gets out of the box.

Canvas agent invoke

Kick off a single-turn run of the Honeycomb Canvas agent.

Canvas agent poll response

Poll for the result of a previously-issued canvas_agent_invoke call.

Create board

Create a board (dashboard) with query, SLO, and text panels.

Create marker

Creates a marker (a vertical annotation on charts) to mark a point or window in time, such as a deploy, incident, or config change.

Create recipient

Create a notification recipient (email, Slack, PagerDuty, or webhook) so it can be attached to triggers and SLO burn alerts.

Create slo

Create an SLO (Service Level Objective) backed by an auto-created SLI derived column.

Create trigger

Create a trigger that fires alerts when a query result crosses a threshold.

Feedback

Submit feedback about Honeycomb's MCP server to the agentic-intelligence team.

Find columns

Search for columns and calculated fields by intent across one or all datasets in an environment.

Find queries

Search query history and saved queries by intent; returns matching queries with their run PKs.

Get aiconversation

Fetch the full event timeline for a single AI conversation, identified by its OpenTelemetry gen_ai.

Get dataset

Get dataset metadata and its full column schema (columns + calculated fields), sorted by most recent write activity.

Get dataset columns

Get the full column schema for one dataset, with optional sample values for specific columns.

Get environment

Get details for a specific environment, including its dataset list sorted by most recent activity.

Get query results

Retrieve results and metadata from an existing query execution.

Get semconv attribute

Get the full definitions of one or more semantic convention attributes by their exact names.

Get span details

Summarize attributes and their common values observed on spans with a specific name.

Get trace

Retrieve all spans for a specific trace ID and render them as a waterfall.

Get triggers

List triggers (alert rules) for the team, or fetch full configuration for a single trigger.

Get workspace context

Call this tool first to orient yourself in a Honeycomb workspace.

List aiconversations

Discover recent AI agent conversations (gen_ai.

List boards

List boards (saved dashboards) in an environment, or fetch one board's full contents by ID.

List recipients

List all pre-registered notification recipients (email, Slack, PagerDuty, webhook) for the team.

List semconv namespaces

List the top-level semantic convention namespaces available in this team's registry.

List spans

List span names in trace data, ranked by count, with how often each is a trace root and which dataset the count came from.

Refinery docs

Read Honeycomb Refinery documentation.

Run bubbleup

Run BubbleUp analysis to find what makes a selected data subset different from the baseline.

Run query

Run a time-series aggregation query against a Honeycomb dataset and return computed results.

Search semconv

Search the semantic convention registry for attributes matching a query.

Update board

Edit an existing board (dashboard) in place.

Update slo

Update an existing SLO.

Update trigger

Update an existing trigger.

FAQ

Frequently asked questions

With a standalone Honeycomb MCP server, the agents and LLMs can only access a fixed set of Honeycomb tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeycomb and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Honeycomb tools.

Yes, absolutely. You can configure which Honeycomb scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Honeycomb data and credentials are handled as safely as possible.

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