How to Use the Unbounce MCP in Pydantic AI

Q: Is this MCP Server reliable for auditing multiple Unbounce sites with Pydantic AI?

It is. The combination of the structured tools (like listpages) and runtime validation ensures your agent can audit dozens of sites without encountering data type errors.

Guarantee accurate Unbounce actions with Pydantic AI's strict typing.

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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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Getting Accurate Lead Data with Pydantic AI

When the agent calls `get_lead`, you don't worry about unexpected fields. The response is validated against a model, guaranteeing that every piece of lead data you receive matches your defined schema. If the API returns corrupted or missing fields, the agent fails loudly with an error—no silent corruption.

Structuring Unbounce Pages via MCP Server

Need to audit pages? The `list_pages` tool pulls structured page metadata. Because Pydantic validates the output, you know exactly what fields are available for every single landing page. The agent can also use `get_page` to pull full content while maintaining type safety; it won't give you garbage JSON.

Inventorying Unbounce Assets with Pydantic AI

Before the agent runs, you need a clear picture of everything. The `list_accounts` and `list_sub_accounts` tools provide validated lists of all clients and accounts. Furthermore, listing available form fields using `list_form_fields` ensures that your agent always knows the correct structure for data capture.

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-alternative-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

It handles them by validating every response from the MCP Server. This means when you ask it to list leads using `list_leads`, the returned data is guaranteed to fit a predictable Python model.

Yes. You can run the agent across multiple sub-accounts, and because of the type validation, you'll get reliable metadata for every single site found using `list_sub_accounts`.

It guarantees correctness. If the underlying Unbounce API changes its structure or returns unexpected data, your agent fails immediately and tells you exactly why—it doesn't just accept bad data.

It is. The combination of the structured tools (like `list_pages`) and runtime validation ensures your agent can audit dozens of sites without encountering data type errors.

The server touches account definitions, page structures, form fields, and lead records. The core sensitive data type managed is customer contact information.

Use it with your favorite AI tools

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