How to integrate Timely MCP with LangChain

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Introduction

This guide walks you through connecting Timely to LangChain using the Composio tool router. By the end, you'll have a working Timely agent that can get your timely account billing details, set up webhook for new time entries, retrieve account info for client project through natural language commands.

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

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

Also integrate Timely with

TL;DR

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

The Timely MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Timely account. It provides structured and secure access to your time-tracking data, so your agent can perform actions like retrieving account information, managing webhooks, and integrating time logs with other workflows on your behalf.

  • Account information retrieval: Instantly fetch up-to-date details about your Timely account, including billing, activity, and user info, for streamlined reporting or troubleshooting.
  • Automated webhook setup: Direct your agent to create new webhooks for your account, enabling real-time integration with external apps and automated event notifications.
  • Seamless workflow automation: Connect Timely events to other services or agents by configuring webhooks, so you can automate time-tracking updates or project triggers.
  • Centralized time management: Allow your agent to coordinate between Timely and your other productivity tools by securely accessing and sharing account data as needed.

Supported Tools & Triggers

Tools
Create ClientTool to create a new client in the specified Timely account.
Create Day LockingTool to create a day locking entry that prevents editing of time entries for specific dates and users.
Create LabelTool to create a new label in the specified Timely account.
Create reportTool to generate reports for a Timely account with optional filters.
Create TeamTool to create a new team in the specified Timely account.
Create WebhookTool to create a new webhook for the specified account.
Delete a labelTool to delete a label by ID from a Timely account.
Delete a teamTool to delete a team by its ID.
Delete WebhookTool to delete an existing webhook by its ID.
Filter reportsTool to filter Timely reports based on date range, users, projects, labels, teams, and billing status.
Get activitiesTool to retrieve all activities (audit trail) for a Timely account.
Get ClientTool to retrieve details of a specific client by its ID.
Get current user's permissionsTool to retrieve the current user's permissions for a specified account.
Get current userTool to retrieve the currently authenticated user.
Retrieve a labelTool to retrieve a label by ID from a Timely account.
Get projectTool to retrieve a single project by its ID.
Retrieve a teamTool to retrieve details of a specific team by its ID.
Retrieve a userTool to retrieve a user by ID from a Timely account.
Get user capacitiesTool to retrieve capacity information for a specific user in a Timely account.
Get user permissionsTool to retrieve a user's permissions by their ID.
Get WebhookTool to retrieve a specific webhook by its ID.
List accountsRetrieve all accessible Timely accounts for the authenticated user.
List clientsTool to list all clients in a Timely account with optional filtering and sorting.
List eventsTool to list all events (time entries) in a Timely account with optional filtering by date range, users, and projects.
List forecastsTool to list all forecasts (tasks) in a Timely account with optional date filtering.
List labelsTool to list all labels in a Timely account.
List project eventsTool to list all events (time entries) for a specific project in Timely.
List projectsTool to list all projects in a Timely account with optional filtering and sorting.
List rolesTool to list all available roles in a Timely account.
List teamsTool to list all teams in the specified Timely account.
List user eventsTool to list all events (time entries) for a specific user in Timely.
List usersTool to list all users in a Timely account with optional filtering and pagination.
List WebhooksTool to list all webhooks in the specified account.
Process bulk eventsTool to create, update, or delete multiple events in a single bulk operation.
Retrieve an accountTool to retrieve details of a specific account by its ID.
Update a clientTool to update an existing client by ID in Timely.
Update day locking settingsTool to update day locking settings for specified users and dates.
Update a labelTool to update a label by ID in a Timely account.
Update a projectTool to update a project by ID in a Timely account.
Update a userTool to update a user by ID in a Timely account.
Update WebhookTool to update an existing webhook by ID.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "timely-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 Timely 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 Timely 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 Timely MCP?

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

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

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

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