How to integrate Listennotes MCP with CrewAI

This guide walks you through connecting Listennotes to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Listennotes connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Listennotes
  • Build a conversational loop where your agent can execute Listennotes 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 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 step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Listennotes connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Listennotes via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_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
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
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 Listennotes MCP URL
6

Create a Composio Tool Router session for Listennotes

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["listennotes"])

url = session.mcp.url
What's happening:
  • You create a Listennotes only session through Composio
  • Composio returns an MCP HTTP URL that exposes Listennotes tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[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:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["listennotes"],
)
url = session.mcp.url

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

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

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

Conclusion

You now have a CrewAI agent connected to Listennotes through Composio's Tool Router. The agent can perform Listennotes 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
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

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. 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 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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