How to integrate Listennotes MCP with Pydantic AI

This guide walks you through connecting Listennotes to Pydantic AI 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 through natural language commands. This guide will help you understand how to give your Pydantic AI 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.

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Listennotes is a powerful podcast search engine with a massive global database. Discover, search, and curate podcasts from around the world in seconds.

26 Tools

Introduction

This guide walks you through connecting Listennotes to Pydantic AI 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 through natural language commands.

This guide will help you understand how to give your Pydantic AI 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.

Also integrate Listennotes with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Listennotes
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Listennotes workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK 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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

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.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Listennotes
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

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

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Listennotes
  • MCPServerStreamableHTTP connects to the Listennotes MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Listennotes
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["listennotes"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Listennotes tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
listennotes_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[listennotes_mcp],
    instructions=(
        "You are a Listennotes assistant. Use Listennotes tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Listennotes endpoint
  • The agent uses GPT-5 to interpret user commands and perform Listennotes operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Listennotes.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Listennotes API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Listennotes and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Listennotes
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["listennotes"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    listennotes_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[listennotes_mcp],
        instructions=(
            "You are a Listennotes assistant. Use Listennotes tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Listennotes.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Listennotes through Composio's Tool Router. With this setup, your agent can perform real Listennotes actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Listennotes for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Listennotes action and event your agent gets out of the box.

Post episodes by ids

The listennotestest_episodes_post endpoint allows users to retrieve metadata for multiple podcast episodes in a single request.

Create podcast via form data

The 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 id

Retrieves detailed information about a specific curated podcast using its unique identifier.

Fetch best podcasts list

The getBestPodcasts endpoint retrieves a curated list of the best podcasts from the Listen Notes platform.

Retrieve genre list

The GetGenres endpoint retrieves a comprehensive list of available genres within the listennotestest platform.

Get playlists

Retrieves a list of playlists from the Listen Notes platform.

Listen to just listen endpoint

The 'just_listen' endpoint is a basic listener or health check mechanism for the listennotestest app.

Get podcast audience by id

Retrieves audience information for a specific podcast identified by its unique ID.

Get curated podcasts

Retrieves a list of curated podcasts from the Listen Notes platform.

Retrieve episode by id

Retrieves detailed information about a specific episode using its unique identifier.

Fetch Podcast Details And Episodes

Retrieves detailed information about a specific podcast using its unique identifier.

Fetch podcast languages

Retrieves a list of supported languages in the Listen Notes API.

Get podcast domains by name

Retrieves a list of podcasts associated with a specified domain name.

Get episode recommendations by id

Retrieves a list of recommended podcast episodes based on a specific episode ID.

Get podcast recommendations by id

Retrieves a list of podcast recommendations based on a specified podcast ID.

Fetch related searches data

Retrieves a list of related search queries based on the current context or user's recent search activity.

Fetch Supported Regions

Retrieves information about available regions in the listennotestest platform.

Retrieve trending searches

Retrieves a list of currently trending search terms related to podcasts.

Search Episode Titles

The search_episode_titles endpoint allows users to search for and retrieve episode titles based on specified criteria.

Search operation endpoint

The search endpoint allows users to query notifications or events within the listennotestest platform.

Fetch Playlist Info

Retrieves detailed information about a specific playlist using its unique identifier.

Post podcast rss by id

Retrieves or generates an RSS feed for a specific podcast identified by its unique ID.

Delete podcast by id

Deletes a specific podcast from the system based on its unique identifier.

Spell check retrieval

The spellcheck endpoint provides a spell-checking service for text input.

Submit podcast rss url

The submit_podcast endpoint allows users to submit a podcast for inclusion in the Listen Notes database.

Get typeahead suggestions

The typeahead endpoint provides real-time search suggestions as users type their queries.

FRAMEWORKS

How to build Listennotes MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Listennotes MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Listennotes MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Listennotes MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Listennotes MCP with Claude Cowork

Codex logo

Codex

Use Listennotes MCP with Codex

OpenClaw logo

OpenClaw

Use Listennotes MCP with OpenClaw

Hermes logo

Hermes

Use Listennotes MCP with Hermes

CLI logo

CLI

Use Listennotes MCP with CLI

Google ADK logo

Google ADK

Use Listennotes MCP with Google ADK

LangChain logo

LangChain

Use Listennotes MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Listennotes MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Listennotes MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Listennotes MCP with LlamaIndex

CrewAI logo

CrewAI

Use Listennotes MCP with CrewAI

FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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