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How to Use the Mashvisor Alternative MCP in Pydantic AI

Build type-safe real estate agents with Pydantic AI and the Mashvisor Alternative.

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Pydantic AI

Connect Mashvisor Alternative MCP to Pydantic AI

Create your Vinkius account to connect Mashvisor Alternative 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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Type-Safe Market Analytics

Bad data ruins investment models. Pydantic AI forces every response from this MCP Server to match your exact schema. When your agent calls `get_city_investment_performance`, the framework validates the return types before your code even sees the numbers. If an API change drops a required field, the system fails loudly. You catch the validation error immediately instead of silently writing corrupted data to your database.

Granular Property Targeting

Stop guessing if a property fits your criteria. Your agent uses `search_properties` to pull initial targets. It then validates the location by passing the output directly into `validate_city` and `get_top_neighborhoods`. Once the area checks out, the deep dive begins. The agent extracts the exact specs via `get_full_property_profile` and verifies the title status using `get_ownership_info`. Every step is strictly typed.

Strict Financial Modeling

You need exact numbers to calculate cap rates. The agent pulls long-term figures using `get_rental_rates` and short-term metrics via `get_airbnb_listings`. Pydantic AI ensures the currency and percentage fields format correctly. Next, it compares those current rates against the past. It grabs 36 months of data through `get_historical_performance` and runs the final numbers through `rento_calculator_lookup` to give you a mathematically sound projection.

Setup guide

Set up Mashvisor Alternative MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "mashvisor-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Mashvisor Alternative MCP in Pydantic AI

Run pip install 'pydantic-ai-slim[mcp]'. Create an MCPToolset pointing to your HTTP endpoint, then pass it in the toolsets array when initializing your Agent.
Standard SDKs trust the JSON payload blindly. Pydantic AI enforces strict runtime validation on MCP tool outputs like get_property_comps, preventing hallucinated fields from crashing your downstream logic.
Yes. The framework is completely model-agnostic. You can route get_marketplace_listings queries through Anthropic for complex reasoning or use a local Llama model for basic data extraction.
The framework throws a validation error immediately. If get_historical_sales returns a string instead of a float for a price, the agent stops execution rather than passing bad data.
The integration processes sensitive yield calculations and historical sales figures. The Vinkius infrastructure operates on a zero-trust model, meaning your authentication token grants access only to the requested endpoint without exposing your underlying architecture.

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