How to Use the Unbounce MCP in Pydantic AI

Q: How does Pydantic AI help manage Unbounce pages?

The pages tool lists available landing pages, but critically, it validates that the returned page IDs and names are structured correctly. This prevents your agent from acting on bad data.

Q: If I use Pydantic AI for Unbounce leads, is the data reliable?

It's highly reliable. The leads tool validates every submission record against a defined schema at runtime. You won’t get hallucinated fields or unexpected types.

Get verified Unbounce data using Pydantic AI—guaranteed correctness for mission-critical tasks.

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Connect Unbounce MCP to Pydantic AI

Create your Vinkius account to connect Unbounce 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 Unbounce with Pydantic AI

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List available Unbounce landing pages

The `pages` tool returns a list of all landing page names and IDs. Because this uses Pydantic validation, you get data that strictly matches the expected schema. This means if the API sends unexpected field names or types, your agent fails loudly—you know immediately when the source data is corrupted.

Verify lead submissions for a page

When you run `leads`, the tool fetches submitted records and validates them against Pydantic models. The resulting list of leads is guaranteed to have the correct fields. Correctness matters more than speed here; this validation prevents silent corruption, which is huge when dealing with client data.

Check Unbounce custom domains

The `domains` tool lists all configured domains. The response is type-safe, guaranteeing you get a clean list of domain names and related identifiers. This certainty means the agent can't proceed with deployment steps if the data structure is wrong.

Setup guide

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

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

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

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Common questions about Unbounce MCP in Pydantic AI

The `pages` tool lists available landing pages, but critically, it validates that the returned page IDs and names are structured correctly. This prevents your agent from acting on bad data.

It's highly reliable. The `leads` tool validates every submission record against a defined schema at runtime. You won’t get hallucinated fields or unexpected types.

The agent fails loudly with a validation error, alerting you immediately. This is much better than having the system silently process corrupted data.

Yes. It's model-agnostic and applies its type safety to any MCP server toolset, including those dealing with domains or lead submissions.

It handles lead submission data. Because it's validated against a schema, you know exactly what fields—like names and emails—are present in every single record.

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