Dotloop MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dotloop as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dotloop. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Dotloop?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Dotloop MCP Server
Connect your AI agent to Dotloop, the leading real estate transaction management platform. This integration allows you to interact with your loops, manage participants, and oversee documents and tasks directly through natural conversation.
LlamaIndex agents combine Dotloop tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Loop Oversight — List and retrieve detailed information for all your real estate transactions
- Participant Management — Add, list, and update profiles for buyers, sellers, and agents involved in a loop
- Document Organization — Explore folders and list metadata for all transaction documents
- Task Tracking — Monitor the status of checklists and to-do items for each deal
- Activity Auditing — Review the full activity log for any specific loop to see historical actions
- Profile Control — Access multiple profiles (personal or brokerage) associated with your account
The Dotloop MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Dotloop to LlamaIndex via MCP
Follow these steps to integrate the Dotloop MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Dotloop
Why Use LlamaIndex with the Dotloop MCP Server
LlamaIndex provides unique advantages when paired with Dotloop through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dotloop tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dotloop tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dotloop, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dotloop tools were called, what data was returned, and how it influenced the final answer
Dotloop + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dotloop MCP Server delivers measurable value.
Hybrid search: combine Dotloop real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dotloop to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dotloop for fresh data
Analytical workflows: chain Dotloop queries with LlamaIndex's data connectors to build multi-source analytical reports
Dotloop MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Dotloop to LlamaIndex via MCP:
add_loop_participant
Add a new participant to a loop
get_loop_activity
Retrieve the activity log for a specific loop
get_loop_details
Get comprehensive information for a specific loop
list_folder_documents
List all documents within a specific loop folder
list_loop_folders
List all document folders within a specific loop
list_loop_participants
List all participants (buyers, sellers, agents) in a specific loop
list_loop_tasks
List all tasks and checklists for a specific loop
list_loops
List all real estate transactions (loops) for a specific profile
list_profile_contacts
List all contacts in the user directory for a specific profile
list_profiles
Retrieve all profiles (brokerages, associations, individual) associated with the user
Example Prompts for Dotloop in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Dotloop immediately.
"List all my active transaction loops."
"Show me the tasks for loop ID '78901'."
Troubleshooting Dotloop MCP Server with LlamaIndex
Common issues when connecting Dotloop to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDotloop + LlamaIndex FAQ
Common questions about integrating Dotloop MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Dotloop with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Dotloop to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.