How to integrate Harvest MCP with LangChain

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Introduction

This guide walks you through connecting Harvest to LangChain using the Composio tool router. By the end, you'll have a working Harvest agent that can create a new client for acme corp, log an expense for project 'website redesign', generate an invoice for hours worked this week, send an estimate message to a client through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Harvest account through Composio's Harvest 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 Harvest project to Composio
  • Create a Tool Router MCP session for Harvest
  • Initialize an MCP client and retrieve Harvest tools
  • Build a LangChain agent that can interact with Harvest
  • 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 Harvest MCP server, and what's possible with it?

The Harvest MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Harvest account. It provides structured and secure access to your time-tracking, invoicing, and project management data, so your agent can create clients, log expenses, send invoices, record payments, and manage estimates automatically on your behalf.

  • Client and contact management: Seamlessly create new clients and add contacts to keep your client list up to date without manual entry.
  • Estimate creation and communication: Automatically generate new estimates, categorize line items, and send estimate messages or updates to clients.
  • Expense tracking automation: Log new expense entries against projects, ensuring accurate financial records and effortless cost tracking.
  • Streamlined invoicing and payments: Create professional invoices, categorize invoice items, send invoice notifications, and record payments as soon as they happen.
  • Project financial workflow optimization: Let your agent handle the full cycle—from creating clients to sending invoices and tracking payments—saving your team valuable time and reducing errors.

Supported Tools & Triggers

Tools
Create ClientTool to create a new client.
Create Client ContactTool to create a new client contact.
Create EstimateTool to create a new estimate.
Create Estimate Item CategoryTool to create a new estimate item category in harvest.
Create Estimate MessageTool to create a new message for an estimate.
Create ExpenseTool to create a new expense entry.
Create InvoiceTool to create a new invoice.
Create Invoice Item CategoryTool to create a new invoice item category.
Create Invoice MessageTool to create a new message for an invoice.
Create Invoice PaymentTool to create a new payment on an invoice.
Create ProjectTool to create a new project.
Create TaskTool to create a new task.
Create Time EntryTool to create a new time entry.
Create UserTool to create a new user.
Delete ClientTool to delete a client.
Delete Client ContactTool to delete a client contact.
Delete EstimateTool to delete an estimate.
Delete Estimate MessageTool to delete an estimate message.
Delete InvoiceTool to delete an invoice.
Delete Invoice Item CategoryTool to delete an invoice item category.
Delete Invoice MessageTool to delete a message from an invoice.
Delete Invoice PaymentTool to delete an invoice payment.
Delete ProjectTool to delete a project.
Delete TaskTool to delete a task.
Delete Time EntryTool to delete a time entry.
Delete UserTool to delete a user.
Get ClientTool to retrieve a specific client by id.
Get Client ContactTool to retrieve a specific client contact.
Get Company InfoTool to retrieve information about the authenticated user's company.
Get EstimateTool to retrieve a specific estimate by id.
Get InvoiceTool to retrieve a specific invoice by id.
Get ProjectTool to retrieve a specific harvest project by id.
Get TaskTool to retrieve a specific task by id.
Get Time EntryTool to retrieve a single time entry by id.
Get UserTool to retrieve a specific user by id.
List Client ContactsTool to list client contacts.
List ClientsTool to list clients.
List Estimate MessagesTool to list messages for an estimate.
List Expense CategoriesTool to list expense categories.
List Invoice Item CategoriesTool to retrieve invoice item categories.
List Invoice MessagesTool to list messages associated with a given invoice.
List Invoice PaymentsTool to retrieve payments for a specific invoice.
List InvoicesTool to list invoices.
List projectsTool to list projects.
List TasksTool to list tasks.
List Time EntriesTool to retrieve a list of time entries.
List UsersTool to list users.
Update ClientTool to update an existing client.
Update Client ContactTool to update a client contact.
Update Company InfoTool to update information about the company.
Update EstimateTool to update an existing estimate.
Update Estimate Item CategoryTool to update an estimate item category.
Update ExpenseTool to update an existing expense.
Update InvoiceTool to update an existing invoice.
Update ProjectTool to update an existing project.
Update TaskTool to update an existing task.
Update Time EntryTool to update an existing time entry.
Update UserTool to update an existing user.

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 Harvest 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 Harvest tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Harvest 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 Harvest tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "harvest-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 Harvest MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Harvest 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 Harvest 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 Harvest 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=['harvest']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "harvest-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 Harvest 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 Harvest 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 Harvest MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Harvest MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Harvest MCP with Claude Agent SDK

Google ADK

Google ADK

Use Harvest MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Harvest MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Harvest MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Harvest MCP with LlamaIndex

CrewAI

CrewAI

Use Harvest MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

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