How do I get a Podbean Access Token?

You must register as a developer at the [**Podbean Developer Portal**](https://developers.podbean.com/). Once you create an application, you can use the OAuth 2.0 flow to generate an access token for your account.

Can I upload audio files through this agent?

The current version of this MCP server is optimized for metadata management and publishing. For initial audio file uploads, we recommend using the Podbean dashboard to obtain a `media_key`, which can then be used by the agent to publish the episode.

Podbean Podcast Hosting MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Podbean Podcast Hosting
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Podbean Podcast Hosting through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Podbean Podcast Hosting "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Podbean Podcast Hosting?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Podbean Podcast Hosting MCP Server

Connect your AI agent to Podbean, the comprehensive podcast hosting and monetization platform. This integration allows you to oversee your podcast catalog, manage episode lifecycles, and audit performance metrics through natural conversation.

Pydantic AI validates every Podbean Podcast Hosting tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Episode Management — List, retrieve, and update details for all your podcast episodes
Content Publishing — Create and publish new episodes or save them as drafts directly via the agent
Deep Analytics — Retrieve download counts and performance trends for entire podcasts or specific episodes
Catalog Oversight — List all podcasts associated with your account and manage their respective IDs
Workflow Automation — Seamlessly delete episodes or update metadata based on listener feedback

The Podbean Podcast Hosting MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Podbean Podcast Hosting to Pydantic AI via MCP

Follow these steps to integrate the Podbean Podcast Hosting MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Podbean Podcast Hosting with type-safe schemas

Why Use Pydantic AI with the Podbean Podcast Hosting MCP Server

Pydantic AI provides unique advantages when paired with Podbean Podcast Hosting through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Podbean Podcast Hosting integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Podbean Podcast Hosting connection logic from agent behavior for testable, maintainable code

Podbean Podcast Hosting + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Podbean Podcast Hosting MCP Server delivers measurable value.

Type-safe data pipelines: query Podbean Podcast Hosting with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Podbean Podcast Hosting tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Podbean Podcast Hosting and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Podbean Podcast Hosting responses and write comprehensive agent tests

Podbean Podcast Hosting MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Podbean Podcast Hosting to Pydantic AI via MCP:

delete_episode

Permanently delete a podcast episode

get_episode_analytics

Retrieve download analytics for a specific episode

get_episode_details

Get comprehensive metadata for a specific podcast episode

get_podcast_analytics

Retrieve download analytics for a specific podcast

list_episodes

Retrieve a list of episodes for the account

list_podcasts

Retrieve all podcasts associated with the authenticated account

publish_episode

Publish a new podcast episode

update_episode

Update an existing podcast episode

Example Prompts for Podbean Podcast Hosting in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Podbean Podcast Hosting immediately.

"List all my podcast episodes."

"Show me the download analytics for episode ID '12345'."

Troubleshooting Podbean Podcast Hosting MCP Server with Pydantic AI

Common issues when connecting Podbean Podcast Hosting to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Podbean Podcast Hosting + Pydantic AI FAQ

Common questions about integrating Podbean Podcast Hosting MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Podbean Podcast Hosting MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.