How to integrate Dialmycalls MCP with CrewAI

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Introduction

This guide walks you through connecting Dialmycalls to CrewAI using the Composio tool router. By the end, you'll have a working Dialmycalls agent that can add a new contact named lisa chen, create a group for emergency alerts, delete the caller id ending in 4321, get my account's remaining message credits through natural language commands.

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

The Dialmycalls MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Dialmycalls account. It provides structured and secure access to your mass notification system, allowing your agent to manage contacts, groups, and broadcast voice or text messages—all on your behalf.

  • Contact and group management: Effortlessly add, update, or remove individual contacts and organize recipients into groups for targeted messaging.
  • Account and sub-account administration: View your main account details, manage access (sub) accounts, and streamline team communication permissions.
  • Broadcast preparation: Set up caller IDs, upload or delete recordings, and get everything ready for your next mass notification campaign.
  • Data cleanup and maintenance: Easily delete old contacts, groups, caller IDs, or recordings to keep your Dialmycalls account organized and up to date.

Supported Tools & Triggers

Tools
Add Access AccountTool to add a new access (sub) account.
Add ContactTool to add a contact to your contact list.
Add GroupTool to add a new contact group.
Delete Access AccountTool to delete an access (sub) account by id.
Delete Caller IDTool to delete a caller id.
Delete ContactTool to delete a contact by id.
Delete GroupTool to delete a contact group by id.
Delete RecordingTool to delete a recording by id.
Get Access AccountTool to retrieve an access (sub) account by id.
Get AccountTool to retrieve your main account details.
Get Caller IDTool to retrieve a caller id by id.
Get ContactTool to retrieve a contact by its unique id.
Get GroupTool to retrieve a contact group by id.
Get RecordingTool to retrieve a recording by id.
List Access AccountsTool to list all access (sub) accounts.
List Caller IDsTool to list all caller ids on the account.
List CallsTool to list all call broadcasts on the account.
List ContactsTool to list all contacts in your contact list.
List Contacts in GroupTool to list contacts by contact group id.
List Do Not ContactsTool to list all do not contact entries.
List GroupsTool to list all contact groups.
List RecordingsTool to list all recordings.
List Text BroadcastsTool to list all outgoing text broadcasts.
List Vanity NumbersTool to list all vanity numbers.
Update Access AccountTool to update an existing access (sub) account by id.
Update Caller IDTool to update an existing caller id by id.

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 Dialmycalls 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 Dialmycalls 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 Dialmycalls MCP URL

Create a Composio Tool Router session for Dialmycalls

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["dialmycalls"],
)
url = session.mcp.url
What's happening:
  • You create a Dialmycalls only session through Composio
  • Composio returns an MCP HTTP URL that exposes Dialmycalls 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="Dialmycalls Assistant",
    goal="Help users interact with Dialmycalls through natural language commands",
    backstory=(
        "You are an expert assistant with access to Dialmycalls tools. "
        "You can perform various Dialmycalls 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 Dialmycalls 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 Dialmycalls 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 Dialmycalls 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_dialmycalls_agent.py

Complete Code

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

python
# file: crewai_dialmycalls_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 Dialmycalls session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["dialmycalls"],
    )
    url = session.mcp.url

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

    # Create Dialmycalls assistant agent
    toolkit_agent = Agent(
        role="Dialmycalls Assistant",
        goal="Help users interact with Dialmycalls through natural language commands",
        backstory=(
            "You are an expert assistant with access to Dialmycalls tools. "
            "You can perform various Dialmycalls 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 Dialmycalls 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 Dialmycalls 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 Dialmycalls through Composio's Tool Router. The agent can perform Dialmycalls 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 Dialmycalls MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use Dialmycalls MCP with Claude Agent SDK

Google ADK

Google ADK

Use Dialmycalls MCP with Google ADK

LangChain

LangChain

Use Dialmycalls MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Dialmycalls MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Dialmycalls MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Dialmycalls MCP with LlamaIndex

FAQ

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

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

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

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

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