How to Use the InvestorLift MCP in Pydantic AI

Q: How do I integrate InvestorLift with Pydantic AI?

Install the pydantic-ai-slim package and use the MCPToolset class. Point it to your HTTP endpoint, and pass the toolset into your Agent definition to begin executing typed calls.

Build type-safe real estate agents with Pydantic AI and InvestorLift to ensure every deal interaction is validated at runtime.

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Connect InvestorLift MCP to Pydantic AI

Create your Vinkius account to connect InvestorLift 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 InvestorLift with Pydantic AI

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Validate InvestorLift deals with Pydantic

Every response from `list_deals` is checked against your Pydantic schemas. If the API returns malformed data, your agent halts immediately. This prevents bad data from poisoning your underwriting models. You get clean, typed objects that your agent can trust to execute accurate bids.

Type-safe buy box management

Use `get_buy_box` and `update_buy_box` to keep your criteria strictly defined. The agent validates your input against your model types before sending updates to the server. This eliminates common errors where incorrect data types break your acquisition logic. You only push valid criteria to your InvestorLift account.

Safe messaging via Pydantic AI

Call `send_message` with full type safety. Your agent confirms the deal ID and message body meet all required constraints before the call fires. This ensures your seller outreach is always formatted correctly. You avoid the risk of sending empty or invalid messages to your contacts.

Setup guide

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

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

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

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Common questions about InvestorLift MCP in Pydantic AI

Install the pydantic-ai-slim package and use the `MCPToolset` class. Point it to your HTTP endpoint, and pass the toolset into your `Agent` definition to begin executing typed calls.

The agent raises a validation error immediately. This stops the process before any faulty data can propagate through your system, ensuring your agent never acts on garbage.

Data is validated in-memory by Pydantic during the transit from the server to your agent. No sensitive deal records are stored on disk or in the validation layer.

Yes. Since the server runs externally, you can update your criteria or logic independently. The agent's schema-based validation adapts to the server's responses at runtime.

You can. Pydantic AI is model-agnostic, so you can point your agent at local models or hosted APIs while keeping the same strict validation for your InvestorLift operations.

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