How to integrate Clientary MCP with CrewAI

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Introduction

This guide walks you through connecting Clientary to CrewAI using the Composio tool router. By the end, you'll have a working Clientary agent that can create new invoice for a client, list all active projects this month, send payment reminder to overdue clients through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Clientary account through Composio's Clientary 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 a Composio API key and configure your Clientary connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Clientary
  • Build a conversational loop where your agent can execute Clientary operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

The Clientary MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Clientary account. It provides structured and secure access so your agent can perform Clientary operations on your behalf.

Supported Tools & Triggers

Tools
Create ClientTool to create a new client record in Clientary.
Create ContactTool to create a new contact within a specified client.
Create ExpenseTool to create a new expense record in Clientary to track expenditures within your account.
Create LeadTool to create a new lead record in Clientary.
Create ProjectTool to create a new project in Clientary with name and rate.
Create TaskTool to create a new task in Clientary.
Delete ClientTool to remove a client and all associated projects, invoices, estimates, and contacts.
Delete LeadTool to permanently delete a lead and all associated Estimates and Contacts.
Delete PaymentTool to remove an existing payment from an invoice.
Delete Payment ProfileTool to remove a specific payment profile from a client's account.
Delete Recurring ScheduleTool to remove a recurring schedule by its identifier.
Get ClientTool to fetch details for a specific client using its ID.
Get ContactTool to retrieve a single contact by its ID.
Get EstimateTool to retrieve details for a single estimate by ID.
Get ExpenseTool to retrieve details for a single expense record in Clientary.
Get Hour EntryTool to obtain details about a specific time entry in Clientary.
Get InvoiceTool to retrieve detailed information for a specific invoice by ID.
Get LeadTool to retrieve a single lead by its ID.
Get ProjectTool to retrieve a single project by its identifier.
Get StaffTool to retrieve a single staff member by their ID.
Get TaskTool to retrieve a specific task by its ID.
List Client ContactsTool to retrieve all contacts for a specific client with pagination support.
List Client ExpensesTool to retrieve all expenses for a specific client within an optional date range.
List Client InvoicesTool to retrieve all invoices for a specific client with pagination support (30 results per page).
List Client ProjectsTool to retrieve all projects associated with a specific client with pagination support (10 results per page).
List ClientsTool to retrieve all clients with pagination support (10 results per page).
List ExpensesTool to retrieve expenses by date range (defaults to current fiscal year).
List LeadsTool to retrieve all leads with pagination support.
List PaymentsTool to retrieve all payments with pagination support (30 results per page).
List Project EstimatesTool to retrieve estimates scoped to a particular project with pagination support (30 results per page).
List Project ExpensesTool to retrieve all expenses for a specific project within an optional date range.
List Project HoursTool to retrieve all time tracking entries logged against a specific project.
List Project InvoicesTool to retrieve all invoices linked to a specific project with pagination support (30 results per page).
List ProjectsTool to retrieve all projects with pagination support (10 results per page).
List StaffTool to retrieve all staff members for an account.
List TasksTool to retrieve all tasks with pagination support (50 results per page).
Send Invoice MessageTool to send an invoice message to recipients via email.
Update ClientTool to update an existing client record in Clientary with partial or complete field modifications.
Update ExpenseTool to update an existing expense record in Clientary with partial or complete field modifications.
Update Hour EntryTool to modify an existing time entry in Clientary with partial or complete field updates.
Update ProjectTool to update an existing project in Clientary with partial or complete field modifications.
Update TaskTool to update an existing task in Clientary with partial or complete field modifications.

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, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Clientary connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Clientary via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env

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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Clientary MCP URL

Create a Composio Tool Router session for Clientary

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["clientary"])

url = session.mcp.url
What's happening:
  • You create a Clientary only session through Composio
  • Composio returns an MCP HTTP URL that exposes Clientary tools

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Clientary and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["clientary"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Clientary through Composio's Tool Router. The agent can perform Clientary operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Clientary MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Clientary MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Clientary MCP with Claude Agent SDK

Google ADK

Google ADK

Use Clientary MCP with Google ADK

LangChain

LangChain

Use Clientary MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Clientary MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Clientary MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Clientary MCP with LlamaIndex

Claude Code

Claude Code

Use Clientary MCP with Claude Code

Codex

Codex

Use Clientary MCP with Codex

FAQ

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

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

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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 Clientary tools.

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

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

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