How to Use the Dotloop MCP in LlamaIndex

Index your real estate transactions with LlamaIndex for semantic search across all your Dotloop loop data.

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Connect Dotloop MCP to LlamaIndex

Create your Vinkius account to connect Dotloop to LlamaIndex 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 LlamaIndex

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Index Dotloop transactions for search

Call `list_loops` and `get_loop_details` to ingest your transaction history into a vector store. Once indexed, you can query your entire history for specific client names or closing dates. This turns your static transaction lists into a queryable knowledge base. You no longer have to remember which folder a specific contract lives in.

Query document contents via LlamaIndex

Use `list_folder_documents` to identify files, then index their metadata to find exactly what you need. Your LlamaIndex agent can now answer questions about document status without you leaving the chat interface. This provides a grounded way to track closing requirements. You get answers based on the actual API results, not guesses.

Track participant data with semantic search

Index your `list_profile_contacts` results so your agent knows exactly who is involved in which transaction. When you ask about a client, the agent pulls their details directly from your Dotloop contacts. This keeps your agent informed about who signed what. It prevents the common issue of confusing contacts across similar transaction names.

Setup guide

Set up Dotloop MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Dotloop MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Dotloop tools.",
)
response = await agent.run("List recent Dotloop data")

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Common questions about Dotloop MCP in LlamaIndex

Yes. By using the MCP tool spec, your agent pulls the data and indexes it into your vector store. You can then run semantic queries against your transactions.

You can trigger a re-index of your loop folders whenever the agent detects a change. This keeps your knowledge base current with the latest activity.

The tools list the documents available in your folders. Your agent can then retrieve the metadata to make those files searchable within your index.

No. Vinkius handles the auth token for you. Your agent only receives an ephemeral session key to access the tools it needs.

Your transaction info is processed in an isolated sandbox. Only you have access to the vector store generated from your specific profile data.

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