How to Use the Landing MCP in Pydantic AI

Q: How do I connect the Landing MCP Server to my Pydantic AI agent?

Instantiate the MCPToolset with your Vinkius HTTP URL and pass it into the Agent constructor. The framework automatically maps the 7 tools to validated Python functions.

Q: Can I use the deprecated HTTP class to connect Landing MCP Server to Pydantic AI?

No. You must use the unified MCPToolset class. The older MCPServerHTTP class is deprecated and will fail when trying to register the landing tools.

Q: How do I verify my active webhooks in Pydantic AI?

The agent calls listlandingwebhooks to get a structured list of active URLs. Pydantic AI validates the array of webhooks against its internal models so your agent can safely inspect them.

Q: How does the Landing MCP Server integration protect my user profile details?

Profile details retrieved via getmylandingprofile are processed strictly in your local runtime memory. Vinkius operates a zero-trust sandbox that never logs your API keys or stores user account metadata.

Validate every lead and page schema at runtime using Pydantic AI to prevent silent failures on your Landing campaigns.

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Connect Landing MCP to Pydantic AI

Create your Vinkius account to connect Landing 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 Landing with Pydantic AI

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Type-safe lead processing with Pydantic AI

The `list_landing_leads` tool fetches captured signups and validates them against strict Python type models at runtime. If a lead payload contains malformed fields, the execution fails immediately. This prevents corrupt email formats or missing phone numbers from entering your databases. Your agent works with clean, validated structured data without manual parsing blocks.

Validate page configurations using this MCP Server

The `list_landing_pages` tool retrieves active design metadata and converts it into validated Pydantic models. Your agent uses this structured data to audit page settings safely. By combining this with `list_landing_projects`, the framework guarantees that every campaign ID and page URL matches your expected schema. This eliminates runtime errors caused by unexpected API payload changes.

Strict webhook management with Pydantic AI

The `create_landing_webhook` tool instantiates real-time event listeners that conform to your exact validation rules. Your agent writes webhook endpoints knowing that the payload schema is verified before deployment. If you need to tear down a pipeline, the agent calls `delete_landing_webhook`. Pydantic AI monitors the response schema, verifying the deletion succeeded before continuing the execution loop.

Setup guide

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

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

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

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Common questions about Landing MCP in Pydantic AI

Instantiate the `MCPToolset` with your Vinkius HTTP URL and pass it into the `Agent` constructor. The framework automatically maps the 7 tools to validated Python functions.

Pydantic AI throws a validation error immediately instead of passing bad data to your agent. This ensures your code never processes incomplete lead records from `list_landing_leads`.

No. You must use the unified `MCPToolset` class. The older `MCPServerHTTP` class is deprecated and will fail when trying to register the landing tools.

The agent calls `list_landing_webhooks` to get a structured list of active URLs. Pydantic AI validates the array of webhooks against its internal models so your agent can safely inspect them.

Profile details retrieved via `get_my_landing_profile` are processed strictly in your local runtime memory. Vinkius operates a zero-trust sandbox that never logs your API keys or stores user account metadata.

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