How to integrate Listennotes MCP with LangChain

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Introduction

This guide walks you through connecting Listennotes to LangChain 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 LangChain 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
  • Connect your Listennotes project to Composio
  • Create a Tool Router MCP session for Listennotes
  • Initialize an MCP client and retrieve Listennotes tools
  • Build a LangChain agent that can interact with Listennotes
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Listennotes functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Listennotes tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Listennotes
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['listennotes']
)

url = session.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
  • This approach allows the agent to dynamically load and use Listennotes tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "listennotes-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Listennotes MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Listennotes tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Listennotes related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['listennotes']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "listennotes-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Listennotes related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Listennotes through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Listennotes MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Listennotes MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Listennotes MCP with Claude Agent SDK

Google ADK

Google ADK

Use Listennotes MCP with Google ADK

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 LangChain?

Yes, you can. LangChain 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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