How to integrate Callingly MCP with OpenAI Agents SDK

Framework Integration Gradient
Callingly Logo
open-ai-agents-sdk Logo
divider

Introduction

This guide walks you through connecting Callingly to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Callingly agent that can activate a client account for onboarding, create an outbound call to new lead, get agent schedule for next week, set up webhook for completed calls through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Callingly account through Composio's Callingly 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 Callingly
  • Configure an AI agent that can use Callingly as a tool
  • Run a live chat session where you can ask the agent to perform Callingly 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 Callingly MCP server, and what's possible with it?

The Callingly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Callingly account. It provides structured and secure access to your Callingly environment, so your agent can automate lead follow-ups, manage agents and clients, trigger outbound calls, and keep your sales workflows moving without manual intervention.

  • Automated outbound call creation: Instantly generate outbound call records so your team can respond to new leads within seconds without lifting a finger.
  • Agent and team management: Let your agent create, delete, or update agents and teams as your sales organization changes and grows.
  • Client onboarding and offboarding: Seamlessly add, activate, deactivate, or remove client accounts as your business requires—no more manual data entry.
  • Real-time webhook setup: Set up and delete webhooks to receive instant notifications for specific call or lead events, keeping your CRM and other tools in sync.
  • Availability and scheduling insights: Retrieve agent schedules to optimize call assignments and guarantee leads get connected when agents are actually available.

Supported Tools & Triggers

Tools
Activate/Deactivate Client AccountTool to activate or deactivate a client account.
Create AgentTool to create a new agent.
Create Outbound CallTool to create a new outbound call record.
Create ClientTool to create a new client.
Create TeamTool to create a new team.
Create WebhookTool to create a new webhook for call or lead events.
Delete AgentTool to delete an agent.
Delete ClientTool to delete a client.
Delete WebhookTool to delete a webhook.
Get Agent ScheduleTool to retrieve the availability schedule for a specific agent.
Get CallTool to retrieve details of a specific call by its id.
Get LeadTool to retrieve details of a specific lead by its id.
Get TeamTool to retrieve details of a specific team.
Get WebhookTool to retrieve details of a specific webhook by its id.
List CallsTool to list calls.
List ClientsTool to list clients.
List LeadsTool to list leads based on provided filters like date range or phone number.
List TeamsTool to list teams.
List Team UsersTool to retrieve a list of agents associated with a specific team.
List UsersTool to retrieve a list of agents.
List WebhooksTool to list configured webhooks.
Remove Team AgentTool to remove a specific agent from a team.
Update AgentTool to update an existing agent's details.
Update Agent ScheduleTool to update an agent's availability schedule.
Update Team Agent SettingsTool to update settings (priority, capacity) for a specific team agent.
Update Team UsersTool to update the list of agents assigned to a team.
Update WebhookTool to update an existing webhook's configuration.

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 Callingly 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 Callingly.

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 Callingly Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["callingly"]
)

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 callingly.
  • The router checks the user's Callingly connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Callingly.
  • This approach keeps things lightweight and lets the agent request Callingly 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 Callingly. "
        "Help users perform Callingly 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 Callingly 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 Callingly 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 Callingly.
  • 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 Callingly 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=["callingly"]
)
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 Callingly. "
        "Help users perform Callingly 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 Callingly MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Callingly.

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.

How to build Callingly MCP Agent with another framework

FAQ

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

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

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

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

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.