How to Use the RentCast MCP in Pydantic AI

Use RentCast with Pydantic AI to get type-safe real estate valuations that never suffer from silent data corruption.

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Connect RentCast MCP to Pydantic AI

Create your Vinkius account to connect RentCast to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup RentCast with Pydantic AI

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Type-safe valuations with Pydantic AI

Every response from `get_rental_valuation` is validated against your Pydantic models at runtime. If the API returns junk, your agent crashes before it can make a bad decision. This level of strictness is mandatory when dealing with financial data. You know exactly what structure to expect, every single time.

Validated comparable analysis

Use `get_sale_comparables` to pull market data directly into your type-checked agent flow. The tool enforces the schema, so your logic never breaks on unexpected fields. It makes your agent code predictable. You don't have to write defensive checks for every possible null or missing key.

Reliable market trend tracking

Fetch data via `list_market_trends` and trust that your Pydantic models will catch any schema drift. It's the safest way to feed external market data into your agents. This creates a clear boundary between the external API and your internal code. You maintain full control over the data quality.

Setup guide

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

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

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

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Common questions about RentCast MCP in Pydantic AI

The MCPToolset handles the mapping. You define your Pydantic models, and the framework validates every tool response against them immediately.

Because you care about correctness. Pydantic AI prevents silent failures by forcing a validation error if the API data doesn't match your expected format.

It uses the standard MCP protocol, so it works perfectly with the current library. Just ensure your server is running and reachable via your defined transport.

You can loop through your property IDs and call the tools sequentially. The type-safe nature ensures that each iteration is validated, preventing bad data from polluting your batch.

All requests are encrypted during transit. Since your models define the schema, the server only ever sees the specific request parameters needed for the lookup, ensuring minimal data exposure.

Use it with your favorite AI tools

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