How to integrate Listennotes MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Listennotes to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Listennotes agent that can find top tech podcasts from last week, get audience stats for this podcast, list curated playlists about entrepreneurship, fetch details for these episode ids through natural language commands.

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

The Listennotes MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Listennotes account. It provides structured and secure access to the Listennotes podcast search platform, so your agent can discover, analyze, and organize podcasts, retrieve episode details, and explore curated recommendations on your behalf.

  • Powerful podcast discovery and search: Let your agent fetch top-rated or genre-specific podcasts, explore curated lists, or search for the best shows to match your interests.
  • In-depth episode and podcast metadata retrieval: Retrieve detailed information about specific episodes or podcasts, including descriptions, publication dates, and audience metrics, to support research or content curation.
  • Bulk data operations for podcasts and episodes: Fetch metadata for multiple podcasts or episodes in a single request, making it easy to keep libraries or dashboards up to date with the latest content.
  • Playlist and curated collection management: Access and organize playlists or curated collections, helping users browse, recommend, or share themed groups of podcasts.
  • Genre exploration and content organization: Retrieve comprehensive genre lists to power advanced filtering, personalized recommendations, or dynamic content categorization.

Supported Tools & Triggers

Tools
Post episodes by idsThe listennotestest episodes post endpoint allows users to retrieve metadata for multiple podcast episodes in a single request.
Create podcast via form dataThe listennotestest podcasts post endpoint allows users to retrieve information about multiple podcasts using various identifiers such as listen notes ids, rss feed urls, apple podcasts ids, or spotify ids.
Retrieve curated podcast by idRetrieves detailed information about a specific curated podcast using its unique identifier.
Fetch best podcasts listThe getbestpodcasts endpoint retrieves a curated list of the best podcasts from the listen notes platform.
Retrieve genre listThe getgenres endpoint retrieves a comprehensive list of available genres within the listennotestest platform.
Get playlistsRetrieves a list of playlists from the listen notes platform.
Listen to just listen endpointThe 'just listen' endpoint is a basic listener or health check mechanism for the listennotestest app.
Get podcast audience by idRetrieves audience information for a specific podcast identified by its unique id.
Get curated podcastsRetrieves a list of curated podcasts from the listen notes platform.
Retrieve episode by idRetrieves detailed information about a specific episode using its unique identifier.
Fetch Podcast Details And EpisodesRetrieves detailed information about a specific podcast using its unique identifier.
Fetch podcast languagesRetrieves a list of supported languages in the listen notes api.
Get podcast domains by nameRetrieves a list of podcasts associated with a specified domain name.
Get episode recommendations by idRetrieves a list of recommended podcast episodes based on a specific episode id.
Get podcast recommendations by idRetrieves a list of podcast recommendations based on a specified podcast id.
Fetch related searches dataRetrieves a list of related search queries based on the current context or user's recent search activity.
Fetch Supported RegionsRetrieves information about available regions in the listennotestest platform.
Retrieve trending searchesRetrieves a list of currently trending search terms related to podcasts.
Search Episode TitlesThe search episode titles endpoint allows users to search for and retrieve episode titles based on specified criteria.
Search operation endpointThe search endpoint allows users to query notifications or events within the listennotestest platform.
Fetch Playlist InfoRetrieves detailed information about a specific playlist using its unique identifier.
Post podcast rss by idRetrieves or generates an rss feed for a specific podcast identified by its unique id.
Delete podcast by idDeletes a specific podcast from the system based on its unique identifier.
Spell check retrievalThe spellcheck endpoint provides a spell-checking service for text input.
Submit podcast rss urlThe submit podcast endpoint allows users to submit a podcast for inclusion in the listen notes database.
Get typeahead suggestionsThe typeahead endpoint provides real-time search suggestions as users type their queries.

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 Listennotes 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 Listennotes.

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 Listennotes Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["listennotes"]
)

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

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 Listennotes MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Listennotes MCP with Claude Agent SDK

Google ADK

Google ADK

Use Listennotes MCP with Google ADK

LangChain

LangChain

Use Listennotes MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Listennotes MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Listennotes MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Listennotes MCP with LlamaIndex

CrewAI

CrewAI

Use Listennotes MCP with CrewAI

FAQ

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

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

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

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

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