How to integrate Bamboohr MCP with Mastra AI

This guide walks you through connecting Bamboohr to Mastra AI using the Composio tool router. By the end, you'll have a working Bamboohr agent that can add new dependent for employee john doe, update direct deposit details for sarah smith, log overtime hours for marketing team members through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Bamboohr account through Composio's Bamboohr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

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

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

Also integrate Bamboohr with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI

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

The BambooHR MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your BambooHR account. It provides structured and secure access to your HR data, so your agent can perform actions like managing employee benefits, updating payroll records, tracking time, and assisting with applicant management on your behalf.

Employee benefits administration: Automatically enroll employees in benefit groups, create or update benefit records, and manage company-wide benefit offerings with ease.
Payroll and direct deposit management: Enable your agent to create paystubs, add unpaid pay periods, and update employee direct deposit information for seamless payroll processing.
Dependent and tax record updates: Empower your agent to add employee dependents and modify withholding details, keeping employee records accurate and compliant.
Time tracking automation: Let your agent log new time tracking records for employees, ensuring precise attendance and overtime data for reporting and payroll.
Applicant and recruitment collaboration: Allow your agent to post comments on applicant records, streamlining feedback and communication during the hiring process.

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:

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

Step-by-step Guide

Step by step09 STEPS

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

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 Bamboohr through MCP.

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

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

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

Create a Tool Router session for Bamboohr

typescript

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

  const composioMCPUrl = session.mcp.url;
  console.log("Bamboohr MCP URL:", composioMCPUrl);

What's happening:

create spins up a short-lived MCP HTTP endpoint for this user
The toolkits array contains "bamboohr" for Bamboohr access
session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

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 Bamboohr toolkit

Create the Mastra agent

typescript

const agent = new Agent({
    name: "bamboohr-mastra-agent",
    instructions: "You are an AI agent with Bamboohr 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

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: {
        bamboohr: 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 Bamboohr 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 Bamboohr 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: ["bamboohr"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

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

Create Candidate Application

Tool to create a candidate application.

Create Job Opening

Tool to create a new job opening in BambooHR ATS.

List Job Applications

Tool to list job applications with optional filters.

Get Benefit Coverages

Tool to retrieve standard benefit coverage options.

Get Member Benefit Events

Tool to list member benefit events.

Get Company EINs

Tool to retrieve company Employer Identification Numbers (EINs).

Get Company Information

Tool to retrieve company information.

Create File Category

Tool to create new company file categories.

Create Time Off Request

Tool to submit a new time off request.

List Datasets

Tool to list available datasets via the Datasets API.

Create Employee Dependent

Tool to add a dependent to an employee.

Get All Employee Dependents

Tool to retrieve all employee dependents.

Create Employee

Tool to create a new employee record.

Create Employee File Category

Tool to create new employee file categories.

Get Changed Employees

Tool to get employees inserted, updated, or deleted since a given timestamp.

List Company Files

Tool to list company file categories and their files.

Upload Company File

Tool to upload a new company file.

Get All Employees

Retrieves all employees from the BambooHR employee directory including their basic information and status.

Get Applicant Statuses

Tool to retrieve applicant statuses.

Get Custom Employee Fields

Tool to fetch custom employee field values.

Run Custom Report

Tool to run a custom report by ID or ad-hoc fields.

Get Employee

Tool to retrieve detailed information for a specific employee.

Get Employee Photo

Tool to retrieve an employee's profile photo by size.

Get Hiring Leads

Tool to retrieve potential hiring leads (employees who can manage job openings) for use in creating a new job opening.

Get Job Summaries

Tool to retrieve a list of ATS job summaries.

Get Departments Metadata

Tool to list department metadata.

Get Meta Divisions

Tool to list all division metadata.

List Employment Status Metadata

Tool to list all employment status metadata.

Get Meta Job Titles

Tool to retrieve job title metadata.

Get Meta Locations

Tool to list location metadata.

Get Time-Off Types Metadata

Tool to list time-off type metadata.

Get Report

Tool to fetch a built-in or published report in JSON or other formats.

Get Time-Off Balances

Tool to retrieve time-off balances for employees.

Get Time-Off Requests

Tool to list time-off requests within a date range.

List Company Reports

Tool to list all available company and custom reports.

Get Country Options

Tool to retrieve all available country options.

Get List Field Details

Tool to get details for all list fields.

Get Tabular Fields Metadata

Tool to list tabular table fields metadata.

Get Users

Tool to list active users with basic info.

Update Employee

Tool to update fields on a specified employee record.

Update Time Off Request

Tool to update the status of an existing time-off request.

FRAMEWORKS

How to build Bamboohr MCP Agent with another framework

OpenAI Agents SDK

Use Bamboohr MCP with OpenAI Agents SDK

Claude Agent SDK

Use Bamboohr MCP with Claude Agent SDK

Claude Code

Use Bamboohr MCP with Claude Code

Claude Cowork

Use Bamboohr MCP with Claude Cowork

Codex

Use Bamboohr MCP with Codex

OpenClaw

Use Bamboohr MCP with OpenClaw

Hermes

Use Bamboohr MCP with Hermes

CLI

Use Bamboohr MCP with CLI

Google ADK

Use Bamboohr MCP with Google ADK

LangChain

Use Bamboohr MCP with LangChain

Vercel AI SDK

Use Bamboohr MCP with Vercel AI SDK

LlamaIndex

Use Bamboohr MCP with LlamaIndex

CrewAI

Use Bamboohr MCP with CrewAI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Ashby

Api Key

Ashby is an applicant tracking system that handles job postings, candidate management, and hiring analytics.

Async interview

Api Key

Async interview is an on-demand video interview platform for streamlined hiring. Candidates record responses on their schedule, so employers can review anytime.

Breathe HR

Api Key

Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.

Connecteam

Api Key

Connecteam is a workforce management platform for deskless teams, streamlining operations, HR, and team communication. It helps businesses save time by automating scheduling, time tracking, and staff engagement tasks.

FAQ

Frequently asked questions

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

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

Start with Bamboohr.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Bamboohr tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Bamboohr MCP with Mastra AI

Connect Bamboohr without auth hassles

Introduction

Also integrate Bamboohr with

TL;DR

What is Mastra AI?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import libraries and validate environment

Create a Tool Router session for Bamboohr

Configure Mastra MCP client and fetch tools

Create the Mastra agent

Set up interactive chat interface

Complete Code

Conclusion

Supported Tools

How to build Bamboohr MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Explore Other Toolkits

Ashby

Async interview

Breathe HR

Connecteam

Frequently asked questions

What are the differences in Tool Router MCP and Bamboohr MCP?+

Can I use Tool Router MCP with Mastra AI?+

Can I manage the permissions and scopes for Bamboohr while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Bamboohr.It takes 30 seconds.

Teams ship real agents on Composio