How to integrate Listennotes MCP with LlamaIndex

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Listennotes Logo
LlamaIndex Logo
divider

Introduction

This guide walks you through connecting Listennotes to LlamaIndex 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 LlamaIndex 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:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Listennotes
  • Connect LlamaIndex to the Listennotes MCP server
  • Build a Listennotes-powered agent using LlamaIndex
  • Interact with Listennotes through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built 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.

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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Listennotes account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Listennotes

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Listennotes access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called listennotes_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

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

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_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")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["listennotes"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Listennotes actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Listennotes actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, listennotes)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Listennotes tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Listennotes database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Listennotes

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Listennotes, then start asking questions.

Complete Code

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

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

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

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
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")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["listennotes"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Listennotes actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Listennotes actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Listennotes to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Listennotes tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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

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

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 LlamaIndex?

Yes, you can. LlamaIndex 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.

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free