Can I check the total play count for an episode using the agent?

Yes! Use the `get_episode` tool with the Episode ID. Your agent will fetch the detailed metadata, which includes the `total_plays` field for that specific episode.

How do I update the description of a podcast episode?

Simply ask the agent to `update_episode` and provide the Episode ID and the new description. The information will be synchronized with Buzzsprout immediately.

Does the integration allow uploading new audio files?

Currently, the toolset focuses on managing metadata, status, and analytics. Creating an episode via `create_episode` allows setting the title and description, but the audio file upload should be managed through the Buzzsprout web interface.

Buzzsprout MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Buzzsprout
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Buzzsprout 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 Buzzsprout "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Buzzsprout?"
    )
    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 Buzzsprout MCP Server

Connect your Buzzsprout account to any AI agent and orchestrate your podcast management, episode creation, and performance tracking through natural conversation.

Pydantic AI validates every Buzzsprout tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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 Oversight — List all your podcast episodes and retrieve detailed metadata, including play counts and audio URLs.
Content Management — Create, update, or delete episodes directly from your workspace with custom titles and descriptions.
Performance Tracking — Monitor all-time play statistics for individual episodes to track your podcast growth.
Podcast Information — Retrieve core podcast details including artwork, website links, and categories.
Account Insights — Access your podcast configuration and settings straight from your workspace.
Deep Dives — Get detailed data for specific episode IDs using natural language.

The Buzzsprout MCP Server exposes 7 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 Buzzsprout to Pydantic AI via MCP

Follow these steps to integrate the Buzzsprout 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 7 tools from Buzzsprout with type-safe schemas

Why Use Pydantic AI with the Buzzsprout MCP Server

Pydantic AI provides unique advantages when paired with Buzzsprout 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 Buzzsprout integration code

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

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

Buzzsprout + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Buzzsprout MCP Server delivers measurable value.

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

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

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

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

Buzzsprout MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Buzzsprout to Pydantic AI via MCP:

create_episode

Create a new podcast episode

delete_episode

Delete an episode permanently

get_account_info

Retrieve core account/podcast settings

get_episode

Get details of a specific episode

get_podcast_info

Retrieve core podcast information

list_episodes

List all podcast episodes

update_episode

Update an existing episode

Example Prompts for Buzzsprout in Pydantic AI

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

"List my last 5 podcast episodes in Buzzsprout."

"How many plays does the 'Tech Trends 2026' episode have?"

"Update the title of episode ep_123 to 'New Improved Title'."

Troubleshooting Buzzsprout MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Buzzsprout + Pydantic AI FAQ

Common questions about integrating Buzzsprout 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 Buzzsprout MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.