How to integrate Honeyhive MCP with Mastra AI

This guide walks you through connecting Honeyhive to Mastra AI using the Composio tool router. By the end, you'll have a working Honeyhive agent that can add new datapoints to your evaluation dataset, list all datasets in your honeyhive project, log a batch of model events for analysis through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Honeyhive account through Composio's Honeyhive MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Honeyhive is an AI observability and evaluation platform for analyzing LLM apps. It helps teams monitor, debug, and improve AI system reliability faster.

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Introduction

This guide walks you through connecting Honeyhive to Mastra AI using the Composio tool router. By the end, you'll have a working Honeyhive agent that can add new datapoints to your evaluation dataset, list all datasets in your honeyhive project, log a batch of model events for analysis through natural language commands.

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

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

Also integrate Honeyhive with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAI

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 Honeyhive tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Honeyhive 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 Honeyhive 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 Honeyhive MCP server, and what's possible with it?

The Honeyhive MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeyhive account. It provides structured and secure access to your AI observability platform, so your agent can perform actions like managing datasets, logging model and tool events, evaluating runs, and configuring project settings on your behalf.

  • Dataset management and organization: Create, retrieve, and delete datasets for your AI projects, helping you maintain organized and up-to-date evaluation data.
  • Efficient event logging: Log batches of model or external tool events, enabling comprehensive tracking and analysis of AI system interactions in real-time.
  • Data curation and cleanup: Add new datapoints to datasets or remove specific datapoints, ensuring your evaluation data remains accurate and relevant.
  • Streamlined evaluation workflows: Mark evaluation runs as completed and fetch project configuration details, making it easy to track progress and update run statuses automatically.

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 Honeyhive 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 Honeyhive

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

  const composioMCPUrl = session.mcp.url;
  console.log("Honeyhive MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "honeyhive" for Honeyhive 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 Honeyhive toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "honeyhive-mastra-agent",
    instructions: "You are an AI agent with Honeyhive 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: {
        honeyhive: 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 Honeyhive 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 Honeyhive 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: ["honeyhive"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "honeyhive-mastra-agent",
    instructions: "You are an AI agent with Honeyhive 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: { honeyhive: 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 Honeyhive 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 Honeyhive action and event your agent gets out of the box.

Add datapoints to dataset

Tool to add datapoints to a dataset.

Compare Experiment Runs

Tool to retrieve experiment comparison between two evaluation runs.

Compare Runs Events

Tool to compare events between two experiment runs side-by-side.

Batch Create Datapoints

Tool to create multiple datapoints in a single batch operation.

Create Batch Model Events

Tool to create multiple model events in a single request.

Create Batch Tool Events

Tool to log a batch of external API calls as tool events.

Create Configuration

Creates a new configuration in HoneyHive for managing LLM or pipeline settings.

Create Datapoint

Tool to create a new datapoint with input-output pairs.

Create Dataset

Tool to create a dataset.

Create Event

Tool to create a new event in HoneyHive to track execution of different parts of your application.

Create Metric

Tool to create a new metric in HoneyHive.

Create Model Event

Tool to create a new model event to log LLM call data.

Create Tool

Creates a new tool definition in a HoneyHive project.

Delete Datapoint

Tool to delete a specific datapoint by its ID.

Delete Dataset

Tool to delete a dataset by ID.

End Evaluation Run

Tool to update an evaluation run's status and metadata.

Get Configurations

Tool to retrieve a list of configurations.

Get Datasets

Retrieve datasets from HoneyHive for a specified project.

Get Events

Tool to query events with filters and projections from HoneyHive.

Get Events By Session ID

Tool to retrieve the complete tree of nested events for a specific session.

Get Events Chart

Tool to retrieve charting and analytics data for events over time.

Get Metrics

Retrieves all metrics associated with a HoneyHive project.

Get Projects

Tool to retrieve all projects in the HoneyHive account.

Get Evaluation Run Details

Tool to get details of an evaluation run by its UUID.

Get Run Metrics

Tool to get event metrics for an experiment run.

Get Evaluation Runs

Tool to retrieve a list of evaluation runs from HoneyHive.

Get Runs Schema

Tool to retrieve the schema for experiment runs in HoneyHive.

Get Session

Retrieve a complete session tree by session ID from HoneyHive.

List Tools

Tool to list all available Honeyhive tools.

Retrieve Datapoint

Retrieve a specific datapoint by its ID from HoneyHive.

Retrieve Datapoints

Retrieve datapoints from a HoneyHive project.

Retrieve Events

Retrieve and export events from a HoneyHive project.

Retrieve Experiment Result

Tool to retrieve the result of a specific experiment run.

Start Evaluation Run

Creates a new evaluation run to group and track multiple session events for analysis.

Start Session

Start a new HoneyHive session for tracing and observability.

Update Configuration

Tool to update an existing HoneyHive configuration.

Update Datapoint

Update an existing datapoint by ID.

Update Dataset

Tool to update an existing dataset.

Update Event

Update an existing HoneyHive event by ID.

Update Metric

Tool to update an existing metric.

Update Project

Updates an existing HoneyHive project's name or description.

Update Tool

Tool to update an existing tool in HoneyHive.

FRAMEWORKS

How to build Honeyhive MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Honeyhive MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Honeyhive MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Honeyhive MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Honeyhive MCP with Claude Cowork

Codex logo

Codex

Use Honeyhive MCP with Codex

OpenClaw logo

OpenClaw

Use Honeyhive MCP with OpenClaw

Hermes logo

Hermes

Use Honeyhive MCP with Hermes

CLI logo

CLI

Use Honeyhive MCP with CLI

Google ADK logo

Google ADK

Use Honeyhive MCP with Google ADK

LangChain logo

LangChain

Use Honeyhive MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Honeyhive MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Honeyhive MCP with LlamaIndex

CrewAI logo

CrewAI

Use Honeyhive MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Honeyhive MCP server, the agents and LLMs can only access a fixed set of Honeyhive tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeyhive 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 Honeyhive tools.

Yes, absolutely. You can configure which Honeyhive 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 Honeyhive data and credentials are handled as safely as possible.

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