How to Use the Beehiiv MCP in Pydantic AI

Q: Can Pydantic AI read my Beehiiv segments?

The listsegments tool exposes your internal subscriber groups. Your agent fetches the segment IDs and validates them against your predefined types.

Q: Does Pydantic AI support Beehiiv automations?

You can query active email journeys using the listautomations tool. The agent reads the workflow states and ensures the response format is strictly correct.

Bring type-safe newsletter management to Pydantic AI using the Beehiiv MCP Server.

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Connect Beehiiv MCP to Pydantic AI

Create your Vinkius account to connect Beehiiv 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 Beehiiv with Pydantic AI

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Type-Safe Subscriber Management

The `create_subscription` and `get_subscription` tools handle reader data with strict validation. You define an MCPToolset pointing to your endpoint, and the framework ensures every response matches your expected schema. Adding a new Beehiiv subscriber requires specific email and tier fields. If the API returns unexpected audience data, your agent fails loudly instead of silently corrupting your internal user database.

Pydantic AI Publication Reads

Your agent pulls newsletter content using the `list_posts` and `get_post` tools. It parses the returned JSON directly into Pydantic models for immediate downstream processing. Pydantic AI's model-agnostic design means you can swap between OpenAI and local models without changing how you access your Beehiiv publications. The MCP Server handles the external API requests while your code focuses on analyzing the actual newsletter text.

Strict Analytics Fetching

The `get_post_stats` tool grabs performance metrics across the MCP connection. The framework validates open rates and click numbers at runtime before your agent makes any decisions based on them. You never have to worry about your agent hallucinating a high open rate. The agent reads the exact Beehiiv statistics from the source and throws a validation error if the metrics structure shifts unexpectedly.

Setup guide

Set up Beehiiv 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": {
        "beehiiv-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Beehiiv MCP in Pydantic AI

Install the slim package with MCP support. Pass your HTTP endpoint to MCPToolset and include it in your agent's toolsets array.

The `list_segments` tool exposes your internal subscriber groups. Your agent fetches the segment IDs and validates them against your predefined types.

You can query active email journeys using the `list_automations` tool. The agent reads the workflow states and ensures the response format is strictly correct.

The unified toolset approach supports Streamable HTTP for external connections. You point it at your Vinkius URL and the framework handles the rest.

Reading draft texts and publication metrics requires no persistent storage on our end. The server executes the request within an isolated sandbox and destroys the environment right after the response completes.

Use it with your favorite AI tools

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