How to Use the LandTech MCP in OpenAI Agents SDK

Q: How do I connect the LandTech MCP Server to the OpenAI Agents SDK?

Install the openai-agents package. Create an MCPServerStreamableHttp instance with your endpoint URL and pass it to your Agent constructor. The SDK auto-discovers all 12 tools instantly.

Q: Can LandTech and the OpenAI Agents SDK track planning applications?

Yes. The searchurbanplanning tool queries local authority databases for active applications. Your agent monitors these records and logs changes directly to your dashboard.

Q: How do I cache LandTech tool lists in the OpenAI Agents SDK?

Set cacheToolsList=True in your MCP Server configuration. This prevents the agent from re-fetching the available tools on every execution loop.

Q: Does LandTech provide building-level data?

The getbuildingdata tool extracts specific property attributes. You get floor areas, structural details, and usage classifications without manually scraping public records.

Build production real-estate agents with OpenAI Agents SDK that analyze land constraints and validate title data.

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Connect LandTech MCP to OpenAI Agents SDK

Create your Vinkius account to connect LandTech to OpenAI Agents SDK 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 LandTech with OpenAI Agents SDK

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Claude

Perplexity

Automate Site Screening with OpenAI Agents SDK

Your agent uses `search_land_parcels` to find development opportunities based on specific geospatial coordinates. It immediately runs those coordinates through `get_site_constraints` to flag flood zones or conservation areas before you spend a dime on manual research. The OpenAI tracing dashboard logs every API call. You see exactly why your site acquisition agent rejected a plot, keeping the entire screening process visible and auditable.

Pull Title Records and Market Comps

The `get_ownership_title` tool fetches the registered freehold or leasehold owner for any property. Your agent combines this with `get_price_comparables` to build a baseline financial model based on recent local sales. Handoffs let you separate tasks. One specialized agent handles the legal ownership extraction while another crunches the market data, passing the consolidated report to your main MCP Server pipeline.

Audit Local Planning History

Running `get_planning_details` pulls the active application history for a specific site. Your agent cross-references this against regional rules using `list_local_authority_plans` to check if a proposed development fits current zoning laws. Built-in guardrails in the SDK ensure the agent stops and asks for human approval if the planning data contradicts your strict investment parameters. You control the final go/no-go decision.

Setup guide

Set up LandTech MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all LandTech tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives LandTech tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate LandTech tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="LandTech Agent",
            instructions="You have access to LandTech tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about LandTech MCP in OpenAI Agents SDK

Install the `openai-agents` package. Create an `MCPServerStreamableHttp` instance with your endpoint URL and pass it to your Agent constructor. The SDK auto-discovers all 12 tools instantly.

Yes. The `search_urban_planning` tool queries local authority databases for active applications. Your agent monitors these records and logs changes directly to your dashboard.

Set `cacheToolsList=True` in your MCP Server configuration. This prevents the agent from re-fetching the available tools on every execution loop.

The `get_building_data` tool extracts specific property attributes. You get floor areas, structural details, and usage classifications without manually scraping public records.

This integration queries public planning applications and registered ownership titles. The V8 Isolate Sandbox destroys the execution environment after every request, ensuring no residual data bleeds between sessions.

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