How to Use the LandTech MCP in LangChain

Q: How do I connect the LandTech MCP Server to LangChain?

Install langchain-mcp-adapters. Use MultiServerMCPClient with the LandTech endpoint URL to load the tools into your environment.

Q: Can LangChain agents automatically check LandTech site constraints?

Yes. You bind getsiteconstraints to your agent. The model evaluates the returned risk factors and decides whether to continue the chain or reject the site.

Q: How do I persist saved sites across LangChain sessions?

Call listsavedsites within your workflow to pull your portfolio. Use client.session() to keep that context active across multiple turns of conversation.

Build automated land appraisal pipelines in LangChain by chaining real estate intelligence and ownership data.

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Connect LandTech MCP to LangChain

Create your Vinkius account to connect LandTech to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain LandTech MCP Server tools for site filtering

The `search_land_parcels` tool feeds raw coordinate data directly into your LangChain pipeline. Your agent takes those boundaries and immediately passes them to `get_site_constraints` to flag flood zones or conservation limits before you waste time on a bad plot. The math doesn't lie. You build a sequential ReAct agent that evaluates viability step-by-step. If the constraints check passes, the chain triggers `get_ownership_title` to pull the freehold registry details. Every API request and token gets logged in LangSmith for full observability.

Automate planning policy research

The `list_local_authority_plans` tool extracts regional zoning rules so your agent knows exactly what a council allows. You pass that output into `get_planning_policy` to isolate the specific residential or commercial guidelines for your target area. No more digging through PDF council documents. Your setup queries the active planning database, runs the text through an LLM to summarize density limits, and outputs a hard go/no-go recommendation for the site.

Calculate market comparables instantly

The `get_price_comparables` tool fetches recent sales data for properties matching your target criteria. Your agent combines this with `get_real_estate_market_data` to build a regional pricing model. You get a baseline financial projection without opening a spreadsheet. The chain cross-references local market velocity with `get_building_data` to confirm square footage, giving your acquisition team the hard numbers they need to make an offer.

Setup guide

Set up LandTech MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes LandTech tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "landtech-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent LandTech transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LandTech. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LandTech MCP in LangChain

Install `langchain-mcp-adapters`. Use `MultiServerMCPClient` with the LandTech endpoint URL to load the tools into your environment.

Yes. You bind `get_site_constraints` to your agent. The model evaluates the returned risk factors and decides whether to continue the chain or reject the site.

You get direct access to local authority records. Tools like `get_planning_details` and `search_urban_planning` pull active applications and historical decisions for any parcel.

Call `list_saved_sites` within your workflow to pull your portfolio. Use `client.session()` to keep that context active across multiple turns of conversation.

The server processes raw geospatial coordinates and public registry titles. Vinkius runs the connection inside a zero-trust V8 Isolate Sandbox, meaning your API tokens and queried parcel IDs disappear the moment the session ends.

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