How to Use the Audienceful MCP in Pydantic AI

Q: Can Pydantic AI search for specific Audienceful subscribers?

The agent calls listpeople and passes an email address or status filter. The framework strictly validates the returned list against your expected schema.

Connect the Audienceful MCP Server to Pydantic AI and manage email subscribers with strict runtime validation and zero hallucinated payloads.

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Connect Audienceful MCP to Pydantic AI

Create your Vinkius account to connect Audienceful 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 Audienceful with Pydantic AI

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Manage Audienceful via Pydantic AI

Adding users to your newsletter relies on the `create_person` and `update_person` tools. Your agent compiles the contact details, but the framework validates every single string against your Pydantic models before the request leaves your machine. This strict typing ensures no malformed email addresses ever reach your marketing database. Pulling individual records requires `get_person` and a valid UID. When a user requests account deletion, the system executes `delete_person` to remove them entirely. If the API returns unexpected JSON during these calls, the application fails loudly instead of silently corrupting your state.

Validate custom field creation

Building new contact properties uses `create_custom_field` alongside `list_custom_fields`. The agent checks existing tags to avoid naming collisions before generating a new one. Because this MCP Server strictly types its inputs, you never end up with duplicate or misspelled column names. Dropping unused attributes happens through `delete_custom_field`. You pass the target ID, and the operation executes cleanly. The unified MCP protocol handles the underlying HTTP connection without requiring deprecated server wrappers.

Trigger campaigns predictably

Kicking off an email sequence uses the `trigger_automation` tool. The agent identifies a specific user behavior and fires the corresponding workflow ID. Model-agnostic design means you can swap between Anthropic or local models without breaking this marketing logic. Gathering analytics requires the `list_send_reports` tool to fetch recent delivery stats. The framework forces the returned metrics into your predefined data structures. Integrating this MCP Server guarantees your automated marketing pipelines run exactly as coded.

Setup guide

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

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

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

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Common questions about Audienceful MCP in Pydantic AI

Install the slim MCP package and initialize an MCP toolset with your Vinkius URL. Pass that object into the toolsets parameter of your Agent instance.

The agent calls list_people and passes an email address or status filter. The framework strictly validates the returned list against your expected schema.

Your application throws a validation error immediately. The framework prioritizes correctness over speed and refuses to process unmapped JSON fields.

Yes. The underlying client handles both SSE and Streamable HTTP transports natively. You just provide the external server URL.

Vinkius hosts the tool logic in a single-use V8 isolate that blocks all unauthorized network access. Your user UIDs and automation logs pass through a strictly authenticated endpoint. The container dies the second your request finishes.

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