How to Use the Urbanise MCP in LangChain

Q: What data types can LangChain process from Urbanise?

LangChain can work with everything: resident details from listpropertyoccupants, budgets from listpropertybudgets, and infrastructure equipment lists from listfacilityassets. The output of any tool becomes an input for the next step in your chain.

Q: Does LangChain help manage Urbanise webhook configurations?

Yes. You can list configured webhooks using listconfiguredwebhooks within a chain. This lets your agent audit which events are currently triggering automated actions across the property management system.

Automating Multi-Step Property Management Workflows with LangChain.

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Connect Urbanise MCP to LangChain

Create your Vinkius account to connect Urbanise 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 Urbanise with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Orchestrate Complex Maintenance Scheduling

You can build a chain that first verifies connectivity using `check_api_health`, then checks the property plans via `list_property_plans`. Finally, it uses this gathered context to execute `create_maintenance_job` with specific details. This flow allows your agent to make decisions based on multiple data points. It's perfect for multi-step reasoning where one action dictates the next.

Gather Financial and Occupancy Data

Need to know who lives in a building and how much money is involved? Start by listing all occupants with `list_property_occupants`. Then, follow that up by retrieving billing details using `list_financial_data`. The agent chains these calls together so you get a complete picture for reporting. It doesn't just run two tools; it uses the output from the first tool in the second one.

Audit Property Assets and Suppliers

Build an audit chain that starts by listing all facility assets with `list_facility_assets`. Next, it fetches details on who supplies those materials using `list_managed_suppliers`. This allows your agent to cross-reference inventory data against supplier records automatically. You're not just getting two lists; you're building a verifiable supply chain report.

Setup guide

Set up Urbanise 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 Urbanise 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({
    "urbanise-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 Urbanise transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Urbanise MCP in LangChain

You can build chains that sequence multiple tool calls. For example, an agent could list all accounts using `list_accounting_ledgers`, then use those IDs to filter the results of `list_financial_data`. This gives you a structured report based on connected steps.

LangChain can work with everything: resident details from `list_property_occupants`, budgets from `list_property_budgets`, and infrastructure equipment lists from `list_facility_assets`. The output of any tool becomes an input for the next step in your chain.

Absolutely. You can create a multi-step process that first checks system health with `check_api_health`, then lists existing jobs via `list_maintenance_jobs`, and finally uses the context to call `create_maintenance_job`.

Yes. You can list configured webhooks using `list_configured_webhooks` within a chain. This lets your agent audit which events are currently triggering automated actions across the property management system.

This MCP Server touches sensitive client profile information via `get_client_profile`. When building chains, always ensure your agent only accesses necessary fields and handles the output securely.

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