How to integrate Callingly MCP with CrewAI

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Introduction

This guide walks you through connecting Callingly to CrewAI 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 CrewAI 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 a Composio API key and configure your Callingly connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Callingly
  • Build a conversational loop where your agent can execute Callingly 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 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:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Callingly 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 python-dotenv
What's happening:
  • composio connects your agent to Callingly via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools 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
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Callingly MCP URL

Create a Composio Tool Router session for Callingly

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["callingly"],
)
url = session.mcp.url
What's happening:
  • You create a Callingly only session through Composio
  • Composio returns an MCP HTTP URL that exposes Callingly tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Callingly Assistant",
    goal="Help users interact with Callingly through natural language commands",
    backstory=(
        "You are an expert assistant with access to Callingly tools. "
        "You can perform various Callingly operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Callingly MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Callingly operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Callingly related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_callingly_agent.py

Complete Code

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

python
# file: crewai_callingly_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Callingly session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["callingly"],
    )
    url = session.mcp.url

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

    # Create Callingly assistant agent
    toolkit_agent = Agent(
        role="Callingly Assistant",
        goal="Help users interact with Callingly through natural language commands",
        backstory=(
            "You are an expert assistant with access to Callingly tools. "
            "You can perform various Callingly operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Callingly operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Callingly related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Callingly through Composio's Tool Router. The agent can perform Callingly 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 Callingly MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Callingly MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Callingly MCP with Claude Agent SDK

Google ADK

Google ADK

Use Callingly MCP with Google ADK

LangChain

LangChain

Use Callingly MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Callingly MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Callingly MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Callingly MCP with LlamaIndex

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 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 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.

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