How to Use the Green Street MCP in Pydantic AI

Validate institutional real estate and REIT metrics at runtime using Pydantic AI and the Green Street MCP server.

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Connect Green Street MCP to Pydantic AI

Create your Vinkius account to connect Green Street 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 Green Street with Pydantic AI

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Type-Safe REIT Valuations with Pydantic AI

The `get_nav_estimates` tool delivers Net Asset Value data verified against strict runtime schemas. Pydantic AI guarantees that every returned valuation fits your expected data models, preventing silent format errors during calculations. You can also track earnings metrics using `get_earnings_metrics` with the same level of type safety. If the API returns an unexpected data type, your agent fails immediately rather than processing corrupted financial figures.

Map Sector Allocations Safely

The `get_portfolio_breakout` tool extracts geographic and sector exposure for any listed company. Your type-safe agent parses these breakdowns to ensure your portfolio stays within mandated risk limits. This MCP Server setup lets you cross-reference allocations with market grades via `get_market_grades`. You get clean, validated data structures ready for downstream mathematical modeling.

Track Transaction History Without Hallucinations

The `get_historical_transactions` tool provides verified commercial real estate transaction data. Your agent uses this tool to audit asset pricing trends without risk of inventing fake sales. To complete your analysis, pull recent industry updates using `get_news_articles`. Pydantic AI ensures that the article metadata, publication dates, and text content match your strict schemas before your agent reads them.

Setup guide

Set up Green Street 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": {
        "green-street-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Green Street MCP in Pydantic AI

You instantiate the MCPToolset with the HTTP server URL and pass it to your Agent's toolsets list. The framework automatically registers the real estate tools.

Pydantic AI will raise a validation error at runtime. This prevents your agent from making decisions based on malformed real estate metrics.

Yes, the server handles async calls natively. This allows your Pydantic AI agents to fetch multiple market projections simultaneously without blocking.

Yes, you can use the sector listing tool to identify active property types. Your agent can then filter its queries based on those specific sectors.

All requests for REIT financials and NOI projections are executed in ephemeral, zero-trust sandboxes. Vinkius secures the transport layer and deletes your execution environment immediately after the query finishes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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