How to Use the Listen Notes MCP in AutoGen

Q: How do AutoGen agents collaborate to search Listen Notes?

One agent executes queries using searchpodcastsorepisodes while another agent filters the results. They pass the structured JSON payloads back and forth in their conversation thread.

Q: How do AutoGen agents handle genre taxonomies in Listen Notes?

Your researcher agent calls listpodcastgenres to find the correct taxonomy ID. It then shares this ID with other agents to run targeted queries using getbestpodcasts.

Q: How do I register these tools with my AutoGen assistant?

Use the mcpservertools helper with your server's HTTP URL. Pass the generated list of MCP tools directly to your assistant agent's constructor during setup.

Build multi-agent AutoGen teams that debate, search, and analyze global podcast data automatically.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Listen Notes MCP to AutoGen

Create your Vinkius account to connect Listen Notes to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Listen Notes with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Let AutoGen agents debate podcast selection

The `get_best_podcasts` tool returns a curated list of top-performing shows filtered by your chosen genre. This tool gives your AutoGen agents the baseline directory data they need to debate which shows fit your criteria. One agent can analyze the ratings using this tool, while another agent uses `get_podcast_details` to verify the actual episode release frequency. They negotiate and converge on the best recommendations without manual human intervention.

Coordinate deep searches across agent teams

The `search_podcasts_or_episodes` tool allows your agents to run complex queries across millions of episodes and shows. This tool acts as the primary discovery engine for your multi-agent conversation loops. An assistant agent can run the search, while a separate critic agent reviews the output using `get_episode_details`. This collaborative process ensures your application only delivers highly relevant audio recommendations to the end user.

Track trending terms with this MCP Server

The `get_trending_podcast_searches` tool exposes real-time search trends directly to your multi-agent network. This tool helps your agents proactively identify shifts in listener interest and propose new content strategies. By feeding these trends into `get_curated_podcasts`, your agents can automatically compile relevant playlists. The entire workflow runs locally or via hosted endpoints using the standard MCP protocol.

Setup guide

Set up Listen Notes MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Listen Notes tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Listen Notes_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Listen Notes data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Listen Notes_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Listen Notes data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Listen Notes. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Listen Notes now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Listen Notes MCP in AutoGen

One agent executes queries using `search_podcasts_or_episodes` while another agent filters the results. They pass the structured JSON payloads back and forth in their conversation thread.

Yes. You can define specific tool subsets, like only allowing access to `get_episode_details`, and pass that restricted list to individual agents in your team.

Your researcher agent calls `list_podcast_genres` to find the correct taxonomy ID. It then shares this ID with other agents to run targeted queries using `get_best_podcasts`.

Use the `mcp_server_tools` helper with your server's HTTP URL. Pass the generated list of MCP tools directly to your assistant agent's constructor during setup.

Yes. The MCP Server only handles read-only metadata queries like `get_podcast_details`, running all requests through isolated, zero-trust sandboxes that expose no internal credentials.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript