How to integrate Workday MCP with LangChain

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Introduction

This guide walks you through connecting Workday to LangChain using the Composio tool router. By the end, you'll have a working Workday agent that can request vacation days for next week, check my current absence balance, find all open job postings i manage, summarize feedback from recent interviews through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Workday project to Composio
  • Create a Tool Router MCP session for Workday
  • Initialize an MCP client and retrieve Workday tools
  • Build a LangChain agent that can interact with Workday
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

The Workday MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Workday account. It provides structured and secure access to your HR, payroll, and recruiting data, so your agent can perform actions like managing time off, retrieving employee details, tracking job postings, and analyzing interview feedback on your behalf.

  • Automated time off management: Easily create new time off requests or check absence balances for yourself or others, making leave management effortless.
  • Employee data and profile retrieval: Have your agent fetch profile details for the current user or any specified worker to simplify onboarding and HR processes.
  • Comprehensive job posting insights: Instantly retrieve information about job postings, including descriptions and screening questionnaires, to aid recruiters and hiring managers.
  • Interview feedback analysis: Let your agent pull and summarize interview feedback entries to streamline debriefs and improve hiring decisions.
  • Access to holiday and leave events: Quickly get holiday schedules and leave status values for better workforce planning and scheduling.

Supported Tools & Triggers

Tools
Create Time Off RequestCreates a time off request for the specified worker id and initiates the business process.
Get Absence BalanceRetrieves the specified balance of all absence plan and leave of absence types.
Get Balance DetailsRetrieves the specified balance of all absence plan and leave of absence types.
Get Current UserRetrieves the current authenticated worker's profile information from workday.
Get Holiday EventsReturns the holiday events for the specified workers and time period.
Get Interview FeedbackRetrieves feedback entries for a specific interview to prepare debrief notes with highlights and lowlights.
Get Job PostingRetrieves detailed information about a specific job posting including job description.
Get Job Posting QuestionnaireRetrieves screening questions and questionnaires associated with a specific job posting.
Get Leave Status ValuesRetrieves instances that can be used as values for other endpoint parameters in this service.
Get My Job PostingsFinds all job postings assigned to a specific recruiter by analyzing interviews and job requisitions.
Get ProspectRetrieves a single prospect instance for talent matching and best-fit analysis.
Get Prospect EducationsRetrieves the education of a prospect for talent matching and best-fit analysis.
Get Prospect ExperiencesRetrieves the work experience of a prospect for talent matching and best-fit analysis.
Get Prospect Resume AttachmentsRetrieves resume attachments for a specific prospect to help prepare for upcoming interviews.
Get Prospect SkillsRetrieves the skills of a prospect for talent matching and best-fit analysis.
Get Time Off Status ValuesRetrieves instances that can be used as values for other endpoint parameters in this service.
Get Worker Eligible Absence TypesRetrieves a collection of eligible absence types for the specified worker.
Get Worker Leaves of AbsenceRetrieves the leaves of absence for the specified worker using the working absencemanagement v1 api.
Get Worker Time Off DetailsRetrieves a collection of time off details for the specified worker.
Get Worker Valid Time Off DatesRetrieves the valid time off dates for the specified worker id for the given dates.
List Absence BalancesRetrieves the balance of all absence plan and leave of absence type for the specified worker id.
List BalancesRetrieves the balance of all absence plan and leave of absence type for the specified worker id.
List InterviewsRetrieves a list of interviews with job requisition and recruiter assignment details.
List Job PostingsRetrieves a list of job postings from workday recruiting system with filtering options.
List WorkersRetrieves a collection of workers and current staffing information.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Workday functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Workday tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Workday
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['workday']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Workday 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
  • This approach allows the agent to dynamically load and use Workday tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "workday-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Workday MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Workday tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Workday related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Workday and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['workday']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "workday-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Workday related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Workday through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Workday MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Workday MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Workday MCP with Claude Agent SDK

Google ADK

Google ADK

Use Workday MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Workday MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Workday MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Workday MCP with LlamaIndex

CrewAI

CrewAI

Use Workday MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Workday MCP?

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

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Workday tools.

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

Yes, absolutely. You can configure which Workday 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.

How safe is my data with Composio Tool Router?

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 Workday data and credentials are handled as safely as possible.

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