How to Use the Hiro.fm MCP in Pydantic AI

Q: How do I set up Hiro.fm with Pydantic AI?

Install pydantic-ai-slim with the MCP extra. Create an MCPToolset pointing to your endpoint and pass it in the toolsets list to your Agent.

Q: Why use Pydantic AI for Hiro.fm listener management?

It guarantees correctness. When your agent calls createcontact, the framework ensures the email format and name fields match your exact specifications before executing.

Q: Can Pydantic AI read Hiro.fm tags?

The agent executes listtags and forces the response into a validated list of strings. It drops any unexpected data structures immediately.

Build type-safe audio workflows by connecting Pydantic AI to Hiro.fm for validated listener management.

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Connect Hiro.fm MCP to Pydantic AI

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

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Setup Hiro.fm with Pydantic AI

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Perplexity

Type-safe Hiro.fm MCP Server access

Hiro.fm returns complex JSON structures when you call `get_show` or `get_contact`. Pydantic AI validates every single field in these responses against your defined models at runtime. If the API changes or returns a missing field, the agent fails loudly. You never end up with silent data corruption in your database because a listener record lacked an expected ID.

Upload episodes with confidence

The `add_audio` tool requires specific parameters to correctly attach an MP3 to a private feed. Your agent constructs this payload, and the framework guarantees the types match before the request fires. You can check if the upload succeeded by running `list_audio`. The agent parses the returned episode list, ensuring the new track actually made it into the catalog.

Predictable access control

Hiro.fm manages subscriber entry through `grant_access` and `revoke_access` via the MCP server. You pass the exact contact ID and show ID, knowing the framework will reject malformed strings. Checking your user base is just as strict. The agent calls `list_contacts` and maps the output directly into strongly typed Python objects for your application to process.

Setup guide

Set up Hiro.fm MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "hirofm-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Hiro.fm tools.",
)

result = await agent.run("List recent Hiro.fm transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hiro.fm. 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.

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Common questions about Hiro.fm MCP in Pydantic AI

Install pydantic-ai-slim with the MCP extra. Create an `MCPToolset` pointing to your endpoint and pass it in the toolsets list to your Agent.

It guarantees correctness. When your agent calls `create_contact`, the framework ensures the email format and name fields match your exact specifications before executing.

The agent executes `list_tags` and forces the response into a validated list of strings. It drops any unexpected data structures immediately.

Yes, Pydantic AI is model-agnostic. You can manage your private podcast feeds using local LLMs if you prefer not to send prompts to external providers.

This integration touches personally identifiable contact details and private RSS feed URLs. The managed connection uses strict token-based authentication and ephemeral execution, leaving no persistent data behind.

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