How to Use the DoorLoop MCP in Pydantic AI

Q: How do I register the DoorLoop MCP Server with a Pydantic AI agent?

Use the MCPToolset class pointing to your hosted HTTP endpoint. Pass this toolset instance inside the toolsets list when initializing your Pydantic AI agent.

Q: How does this setup protect my DoorLoop work orders and financial logs?

All data retrieved via listworkorders flows through a zero-trust, ephemeral sandbox. Your API keys are encrypted at rest, and no property data is stored after your agent session ends.

Build type-safe property workflows using Pydantic AI and the DoorLoop MCP Server.

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Connect DoorLoop MCP to Pydantic AI

Create your Vinkius account to connect DoorLoop 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 DoorLoop with Pydantic AI

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Enforce runtime validation using Pydantic AI

Stop letting LLMs output corrupt JSON when managing leases. This MCP Server forces your agent to validate all returned data against strict Pydantic models. If `get_lease_details` returns a missing date or an invalid rent amount, your agent catches the validation error immediately instead of passing bad data to your database. This type-safe approach is critical when running operations that write data. When your agent calls `create_work_order`, Pydantic AI ensures the subject and unit IDs are formatted correctly before the API call is executed.

Audit rent rolls with zero silent failures

Financial reports require perfect accuracy. By using `get_rent_roll_report`, your agent parses complex tenant ledgers into structured Python objects. If the model attempts to hallucinate a field that doesn't exist in the DoorLoop schema, the framework fails loudly. You can connect this to any LLM provider, whether you're using Anthropic, OpenAI, or local models. The validation layer sits on your side of the connection, keeping your data clean regardless of which model you choose.

Manage tenant profiles with structured types

Retrieve tenant directories using `list_tenants` and unpack them into typed models. Your agent can instantly filter active residents, check contact info via `get_tenant_details`, and identify units using `list_units` without writing complex parsing logic. This simplifies building internal tools for your property managers. They get a reliable interface where every property detail fetched via `get_property_details` is guaranteed to match your application's internal types.

Setup guide

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

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

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

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Common questions about DoorLoop MCP in Pydantic AI

Use the `MCPToolset` class pointing to your hosted HTTP endpoint. Pass this toolset instance inside the `toolsets` list when initializing your Pydantic AI agent.

The framework raises a validation error immediately. This prevents your agent from processing corrupt data when running tools like `list_leases` or `get_rent_roll_report`.

Yes. Pydantic AI is model-agnostic, meaning you can connect local models to your MCP Server to read records from `list_properties` without sending data to third-party APIs.

No. Vinkius hosts the server externally, so your Python script only needs the secure HTTP endpoint URL to access tools like `list_vendors`.

All data retrieved via `list_work_orders` flows through a zero-trust, ephemeral sandbox. Your API keys are encrypted at rest, and no property data is stored after your agent session ends.

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