How to Use the DealMachine MCP in Pydantic AI

Q: How do I load DealMachine into Pydantic AI?

Install the pydantic-ai-slimmcp package. Create an MCPToolset pointing to your HTTP endpoint. Pass that into your Agent's toolsets array.

Build type-safe real estate agents with Pydantic AI to validate DealMachine data at runtime.

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Connect DealMachine MCP to Pydantic AI

Create your Vinkius account to connect DealMachine 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 DealMachine with Pydantic AI

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Perplexity

Ingest real estate leads with strict types

Bad data kills real estate deals before they even start. Pydantic AI forces your agent to validate each field it gets back from the API. When you run `add_new_lead`, the framework ensures the property address matches your exact schema requirements. Silent failures are unacceptable in production. If the API returns a malformed response when calling `get_lead_details`, the agent throws a loud validation error. You fix the issue instead of pushing corrupted contact info into your downstream systems.

Validate property equity with this MCP Server

Financial data requires absolute precision. Your agent connects to the DealMachine MCP Server to pull estimated equity and mortgage histories. It executes `get_property_details` and instantly maps the raw JSON into your strict Pydantic models. Managing large lists of tracked assets works the exact same way. The system calls `list_tracked_properties` and guarantees that all internal identifiers are formatted correctly. You spend zero time debugging type errors.

Audit marketing campaigns and driving teams

Tracking outbound mailers means handling dates, recipient counts, and engagement percentages. Your agent uses `list_marketing_campaigns` to grab the raw stats. Pydantic AI converts those messy date strings into native Python datetime objects automatically. Team management also benefits from strict schemas. Running `list_driving_teams` returns active drivers and their specific roles. If a role type changes upstream, your agent catches it immediately and halts execution until you update your models.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DealMachine. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DealMachine MCP in Pydantic AI

Install the pydantic-ai-slim[mcp] package. Create an MCPToolset pointing to your HTTP endpoint. Pass that into your Agent's toolsets array.

Correctness matters more than speed when dealing with property records. Standard clients might silently drop missing fields. Pydantic AI guarantees that the data matches your expected structure.

You can swap between OpenAI, Anthropic, or local models without rewriting your tool logic. The MCP integration handles the API communication while Pydantic enforces the types regardless of the LLM you choose.

The unified MCPToolset approach supports both Streamable HTTP and SSE transports. Point it at your Vinkius endpoint and the framework handles the connection lifecycle.

The Vinkius platform handles your authentication securely. When your agent fetches owner phone numbers or email addresses, the transaction occurs inside an ephemeral V8 Isolate. The sandbox vanishes the second the data transfer completes.

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