How to Use the Dotloop MCP in CrewAI

Q: How do I configure Dotloop tools for a CrewAI agent?

Pass the HTTP URL directly into the agent's mcps array. For advanced setups, use the MCP server class to filter which tools, like listloops, are exposed to specific agents.

Q: Can a CrewAI agent manage Dotloop checklists?

Yes, an agent can call listlooptasks to read all active checklists. The agent can then assign tasks or alert team members about incomplete items.

Q: Does this integration support custom profiles?

Yes, agents can use listprofiles to identify the correct office or brokerage profile. This ensures the crew operates within the correct organizational scope.

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Setup Dotloop with CrewAI

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Multi-agent real estate teams in CrewAI

This MCP Server provides `list_loops` and `get_loop_details` so your specialized agents split up transaction management duties. One agent scans active loops for missing files, while another agent drafts follow-up emails to buyers. CrewAI coordinates this work through shared memory. The agents share context about the transaction state, allowing them to collaborate on compliance audits without stepping on each other's toes.

Automated document compliance audits

The `list_loop_folders` and `list_folder_documents` tools allow your compliance agent to inspect every file uploaded to a transaction. The agent matches the files against state real estate commission requirements. If a folder lacks the mandatory disclosures, the agent escalates the issue to a human coordinator. This keeps your brokerage audit-ready at all times.

Roster management and activity tracking

You use `list_loop_participants` and `get_loop_activity` to monitor who is accessing active transaction files. Your monitoring agent scans the activity log to detect unauthorized edits or missing participant signatures. When a new party needs to be added, the agent executes `add_loop_participant` to update the transaction. This keeps the loop roster accurate throughout the closing process.

Setup guide

Set up Dotloop MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Dotloop tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Dotloop Analyst",
    goal="Access and analyze Dotloop data via MCP.",
    backstory="Expert analyst with direct Dotloop access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Dotloop transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Dotloop Analyst",
        goal="Access and analyze Dotloop data via MCP.",
        backstory="Expert analyst with direct Dotloop access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Dotloop transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dotloop. 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 Dotloop MCP in CrewAI

Pass the HTTP URL directly into the agent's `mcps` array. For advanced setups, use the MCP server class to filter which tools, like `list_loops`, are exposed to specific agents.

Yes, an agent can call `list_loop_tasks` to read all active checklists. The agent can then assign tasks or alert team members about incomplete items.

Agents share tool output through the framework's memory system. If one agent calls `get_loop_details`, the resulting transaction data is accessible to other agents in the crew.

Yes, agents can use `list_profiles` to identify the correct office or brokerage profile. This ensures the crew operates within the correct organizational scope.

All activity logs, file metadata, and transaction details are routed through encrypted HTTPS connections. Vinkius isolates each MCP session in a sandboxed V8 environment, preventing any data leakage between agent runs.

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