How to Use the Dotloop MCP in LangChain

Chain your real estate data directly into LangChain agents for automated transaction management and document tracking.

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Connect Dotloop MCP to LangChain

Create your Vinkius account to connect Dotloop 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 Dotloop with LangChain

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Claude

Perplexity

Automate loop setup with LangChain

Feed your transaction data into a chain to trigger `add_loop_participant` or `list_loops` without manual input. Your agent handles the sequence, ensuring every participant gets added as soon as the profile data hits your system. This removes the manual lag between receiving a lead and creating the file. By chaining `list_profiles` with loop creation, you ensure your workspace is ready before the agent even finishes its first thought.

Monitor transaction status via agents

Connect `get_loop_details` and `get_loop_activity` to your LangGraph pipelines to get real-time updates on closing progress. The agent parses the activity log, identifies blockers, and reports back on specific folder status. Stop digging through the web interface for updates. Let the agent pull the data using `list_loop_tasks` so you only act when a deadline actually approaches.

Sync documents across your workflow

Use `list_loop_folders` and `list_folder_documents` to pull file lists into your LangChain context. The agent can verify if required documents exist in the loop folder before it triggers the next step in your closing process. This ensures your compliance checks happen before the document ever reaches an escrow officer. If a folder is missing a required file, the agent flags it immediately based on the data returned by the tool.

Setup guide

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

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Common questions about Dotloop MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You then pass the tool list directly into your agent constructor to start executing commands.

Yes. Each tool is designed to return data that acts as input for the next step. You can chain a profile search directly into a loop listing.

Your server respects the permissions defined in your account. The tools only return data that your authenticated user token is authorized to see.

The server returns a standard error code that your agent can catch. You can then configure your graph to retry the task or alert you.

Your data remains within the isolated sandbox provided by Vinkius. We use ephemeral sessions so no transaction logs are stored after your agent finishes its run.

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