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How to integrate Bamboohr MCP with Pydantic AI

Connect Pydantic AI to Bamboohr MCP. Add new dependent for employee john doe, update employee details for sarah smith, and more using natural language, with authentication handled for you.

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BambooHR is a cloud-based HR management platform for small and mid-sized businesses. It streamlines employee data, HR workflows, and reporting in one easy interface.

42 Tools

Introduction

This guide walks you through connecting Bamboohr to Pydantic AI using the Composio tool router. By the end, you'll have a working Bamboohr agent that can look up and update employees, manage time off requests, create job openings, track applications, and run custom reports through natural language commands.

This guide will help you understand how to give your Pydantic 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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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Bamboohr
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Bamboohr workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 employee records, hiring data, time off, benefits, reports, and company files, so your agent can manage employees, track candidates, submit time off requests, run reports, and organize HR documents on your behalf.

  • Employee records and organization: Have your agent retrieve or create employee records, find recent employee changes, and review departments, divisions, locations, job titles, and employment statuses.
  • Hiring and candidate management: Direct your agent to create job openings and candidate applications, list applications, check applicant statuses, and review hiring leads or job summaries.
  • Time off administration: Let the agent submit time off requests, review requests within a date range, check employee balances, and inspect available time off types and policies.
  • Reports, benefits, and company data: Instruct your agent to run company reports, review benefit coverages and member events, or retrieve company information and tax identifiers.
  • HR files and employee details: Have your agent list or upload company files, create file categories, retrieve employee photos and custom fields, or add and review employee dependents.

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:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Bamboohr
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Bamboohr
  • MCPServerStreamableHTTP connects to the Bamboohr MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Bamboohr
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["bamboohr"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Bamboohr tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
bamboohr_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[bamboohr_mcp],
    instructions=(
        "You are a Bamboohr assistant. Use Bamboohr tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Bamboohr endpoint
  • The agent uses GPT-5 to interpret user commands and perform Bamboohr operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Bamboohr.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Bamboohr API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Bamboohr and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Bamboohr
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["bamboohr"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    bamboohr_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[bamboohr_mcp],
        instructions=(
            "You are a Bamboohr assistant. Use Bamboohr tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Bamboohr.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Bamboohr through Composio's Tool Router. With this setup, your agent can perform real Bamboohr actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Bamboohr for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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

Lists member benefit enrollment events for employees and their dependents over the past year, with optional offset and limit on returned members.

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

Lists available datasets via the BambooHR Datasets API, returning dataset names and labels for discovery before querying.

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

Get employee IDs grouped by inserted, updated, or deleted changes since a given timestamp.

List Company Files

List company file categories and their files that the requesting user is permitted to see.

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

Lists employee field definitions available in the account, including standard and custom fields.

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

Retrieve an employee's profile photo by ID and size, returning its content as Base64 with a filename and MIME type.

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

Retrieve ATS job opening summaries, optionally filtered by status group and sorted.

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

Retrieve estimated time-off balances for specified employees as of an optional end date.

Get Time-Off Policies

List time-off policies, with accrual and rollover details when available.

Get Time-Off Requests

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

List Company Reports

Lists a page of saved custom reports available in the account.

Get Country Options

Retrieve BambooHR country options for use when updating or validating country fields.

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

List company users with basic information, including IDs, names, email addresses, and account status.

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.

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AutoGen

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FAQ

Frequently asked questions

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