How do I get an API Access Token for Dotloop?

You must register as a developer at the [**Dotloop Developer Center**](https://info.dotloop.com/developers). Once your application is approved, you can utilize OAuth 2.0 to generate an access token for your account.

Does this tool support document signing?

This MCP server is optimized for data retrieval, oversight, and management of participants/tasks. For the actual signing process, you should continue to use the Dotloop web or mobile application.

Dotloop MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Dotloop
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dotloop through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Dotloop "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Dotloop?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Dotloop tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Dotloop MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Dotloop MCP Server

Pydantic AI provides unique advantages when paired with Dotloop through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Dotloop integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Dotloop connection logic from agent behavior for testable, maintainable code

Dotloop + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Dotloop MCP Server delivers measurable value.

Type-safe data pipelines: query Dotloop with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Dotloop tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Dotloop and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Dotloop responses and write comprehensive agent tests

Dotloop MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Dotloop to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Dotloop to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dotloop + Pydantic AI FAQ

Common questions about integrating Dotloop MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Dotloop MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.