How to Use the Dotloop MCP in Pydantic AI

Bring type-safe Dotloop automation to your Pydantic AI agents for reliable transaction handling.

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Connect Dotloop MCP to Pydantic AI

Create your Vinkius account to connect Dotloop to Pydantic AI 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 Pydantic AI

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Fetch Dotloop loop details safely

Use `get_loop_details` to pull specific data about a transaction. Pydantic AI validates the response against your strict schema models before your agent processes it. This prevents runtime errors caused by unexpected API changes. You get clean, validated data every time you query a loop.

List Dotloop contacts with validation

Your agent calls `list_profile_contacts` to gather participant information. The framework checks that every contact field matches your expected format. If the data is malformed, the agent stops immediately. This ensures your records remain consistent and free of corrupted entries.

Query Dotloop transactions accurately

The `list_loops` tool provides an array of your current deals. Pydantic AI ensures the output is typed and verified before it reaches your agent's reasoning loop. This type-safety is critical for production systems. You avoid hallucinated fields because the framework rejects any response that doesn't fit your model.

Setup guide

Set up Dotloop MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "dotloop-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Dotloop tools.",
)

result = await agent.run("List recent Dotloop transactions")
print(result.output)

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Common questions about Dotloop MCP in Pydantic AI

Define your Pydantic models and the MCP toolset will validate incoming data. If the Dotloop API returns an unexpected field, the agent will raise a validation error.

Yes, it supports SSE and HTTP transports. You simply pass the toolset to your agent, and it handles the connection to your Vinkius server.

Absolutely. Since the MCP server is agnostic, you can point your Pydantic AI agent at any model while using the same Dotloop tools.

The framework reports validation failures immediately. You will see exactly which field failed to match your model, allowing for quick fixes.

Vinkius manages your access via a secure, ephemeral tunnel. Your participant contact info is validated locally and never leaked, ensuring your compliance with data privacy standards.

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