How to integrate Onedesk MCP with LangChain

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Introduction

This guide walks you through connecting Onedesk to LangChain using the Composio tool router. By the end, you'll have a working Onedesk agent that can log two hours on today's support ticket, remove outdated attachment from project alpha, delete task 'update onboarding guide' from project through natural language commands.

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

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

Also integrate Onedesk with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Onedesk project to Composio
  • Create a Tool Router MCP session for Onedesk
  • Initialize an MCP client and retrieve Onedesk tools
  • Build a LangChain agent that can interact with Onedesk
  • 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 Onedesk MCP server, and what's possible with it?

The Onedesk MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Onedesk account. It provides structured and secure access to your help desk and project management workspace, so your agent can perform actions like managing tickets, handling tasks, logging work, and cleaning up projects on your behalf.

  • Automated worklog entry creation: Let your agent log time spent on tickets, tasks, or projects, so you can track team effort without manual entry.
  • Ticket and task cleanup: Direct your agent to delete tickets or tasks that are no longer needed, keeping your workspace organized and up to date.
  • Project and requirement management: Have the agent remove outdated projects or requirements, ensuring your portfolio stays relevant and clutter-free.
  • Attachment and comment removal: Ask your agent to delete attachments or comments from tasks, tickets, or projects, maintaining a clean and focused workflow.
  • Customer and timesheet handling: Enable your agent to securely delete customers or timesheets, helping you maintain accurate records and compliance.

Supported Tools & Triggers

Tools
Create Worklog EntryCreate a worklog entry to track time spent on a work item (task, ticket, project, etc.
Delete AttachmentDelete a specific attachment from OneDesk by its ID.
Delete CommentDeletes a comment from OneDesk by its ID.
Delete CustomerDeletes a customer from OneDesk using the DELETE /customers/{id} endpoint.
Delete MessageDeletes a message from OneDesk by its unique message ID.
Delete ProjectDelete a project in OneDesk by its ID.
Delete RequirementDelete a requirement from OneDesk.
Delete TaskTool to delete a specific task.
Delete TicketDeletes a ticket from OneDesk by its internal ID.
Delete TimesheetTool to delete a timesheet by its ID.
Delete UserDeletes a user from OneDesk by their unique user ID.
Get AttachmentsRetrieve attachments for a specific item by its external ID.
Get Comment DetailsRetrieve detailed information for a specific comment by its ID.
Get CommentsAttempts to retrieve comments (discussion posts) from OneDesk.
Get Feedback ItemsRetrieve all feedback items from OneDesk.
Get Feedback DetailsRetrieve detailed information about a specific item in OneDesk by its internal ID.
Get Issue DetailsRetrieve comprehensive details of a specific issue by its ID.
Get IssuesRetrieve a list of issues from OneDesk.
Get MessagesTool to retrieve a list of messages from OneDesk.
Get RequirementsRetrieve a list of requirements from OneDesk.
Get Task DetailsRetrieves comprehensive details of a specific task/item in OneDesk by its ID.
Get TicketsRetrieve tickets from OneDesk by querying a range of item IDs.
Get Timesheet DetailsTool to retrieve details of a specific timesheet entry.
Get WorklogsRetrieve a list of worklogs from OneDesk with optional filtering and pagination.

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

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "onedesk-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 Onedesk 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 Onedesk 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.

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FAQ

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

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

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

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

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