How to integrate Reply MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Reply to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Reply agent that can list all active email campaigns this week, get contacts in my 'leads q3' list, show all available email templates, retrieve blacklisted domains and emails through natural language commands.

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

The Reply MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Reply.io account. It provides structured and secure access to your sales engagement platform, so your agent can create and manage lists, fetch campaign schedules, organize contacts, and monitor outreach performance for you.

  • Personal list creation and management: Easily instruct your agent to create, organize, or delete personal contact lists to streamline your outreach workflow.
  • Contact organization and retrieval: Have your agent fetch contacts within specific lists, helping you target and personalize campaigns more effectively.
  • Campaign and schedule insights: Ask your agent to retrieve all campaign schedules or see which campaigns a specific contact belongs to, giving you up-to-date visibility into your outreach pipeline.
  • Email account and template discovery: Let your agent pull a list of available email accounts or fetch your personal, team, and community templates for quick access while composing new campaigns.
  • Blacklist monitoring and management: Direct your agent to retrieve the full list of blacklisted domains and emails, so you can ensure compliance and maintain deliverability standards with ease.

Supported Tools & Triggers

Tools
Create Personal ListTool to add a new personal list on the people page.
Delete List By IDTool to delete a list by its id.
Get All ListsTool to retrieve all available people lists.
Get full blacklist of domains and emailsTool to retrieve the full list of blacklisted domains and emails.
Get all campaign schedulesTool to list all campaign schedules (default and user-created).
Get Campaigns For ContactTool to retrieve campaigns a contact belongs to by contact id.
Get Contacts in List by IDTool to retrieve contacts in a specific personal list.
Get Email AccountsTool to retrieve list of all email accounts in your reply.
Get List by IDTool to return a specific people list by its id.
Get templates listTool to retrieve a list of user, team, and community templates.
Mark Contact As FinishedTool to mark a specific contact by email or all contacts under a domain as finished in all campaigns.
Mark Contact as RepliedTool to mark a contact as replied in all campaigns by email or domain.
Move Contacts to ListsTool to move one or more contacts to specified lists.
Remove Domain from BlacklistTool to remove the specified domain from the blacklist.
Remove Email from BlacklistTool to remove the specified email from the blacklist.

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 Reply 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 Reply.

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 Reply Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["reply"]
)

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

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 Reply MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Reply MCP with Claude Agent SDK

Google ADK

Google ADK

Use Reply MCP with Google ADK

LangChain

LangChain

Use Reply MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Reply MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Reply MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Reply MCP with LlamaIndex

CrewAI

CrewAI

Use Reply MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

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Context
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Agent.ai
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