How to integrate Clientary MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Clientary to the OpenAI Agents SDK 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 OpenAI Agents SDK 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 and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Clientary
  • Configure an AI agent that can use Clientary as a tool
  • Run a live chat session where you can ask the agent to perform Clientary operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Clientary project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Clientary.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Clientary Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["clientary"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only clientary.
  • The router checks the user's Clientary connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Clientary.
  • This approach keeps things lightweight and lets the agent request Clientary tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Clientary. "
        "Help users perform Clientary operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Clientary and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Clientary operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Clientary.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Clientary and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["clientary"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Clientary. "
        "Help users perform Clientary operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Clientary MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Clientary.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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Claude Code

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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 OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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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