How to Use the Housecall Pro MCP in Pydantic AI

Q: How do I configure the Housecall Pro MCP Server in Pydantic AI?

Install pydantic-ai-slimmcp. Initialize the integration using MCPToolset with your HTTP URL. Pass this toolset directly into your Agent constructor.

Enforce strict type safety for Housecall Pro MCP API data using Pydantic AI.

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Connect Housecall Pro MCP to Pydantic AI

Create your Vinkius account to connect Housecall Pro 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 Housecall Pro with Pydantic AI

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Perplexity

Validate Housecall Pro MCP Server payloads

Running `list_jobs` or `get_customer` forces every response through strict runtime validation. Field service APIs notoriously return unexpected null values. If the API drops a required field, the framework throws a loud validation error immediately. Your agent never operates on corrupted data. It stops execution rather than hallucinating a missing customer address. You get predictable, typed Python objects instead of raw dictionaries.

Lock down estimate calculations

Pulling active rates via `list_price_list_items` lets Pydantic models verify currency formats and float values. The agent uses this verified data to cross-check pending quotes from `list_estimates`. Quoting errors cost money, and this stops them at the source. Your code defines the exact expected schema. If a technician enters a string into a numeric field in the mobile app, Pydantic AI catches the type mismatch before your agent sends the invoice to accounting.

Audit webhooks and billing records

Running `list_webhooks` verifies your dispatch system is actually receiving updates. The agent checks the configured endpoints and flags any disconnected URLs that might break your integration. It does the same for billing. By running `list_invoices`, the agent pulls the current accounts receivable. Because Pydantic AI is model-agnostic, you route this financial data through Anthropic for analysis or a local model for strict privacy.

Setup guide

Set up Housecall Pro 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": {
        "housecall-pro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Housecall Pro MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize the integration using `MCPToolset` with your HTTP URL. Pass this toolset directly into your Agent constructor.

Raw calls leave you vulnerable to silent schema changes. Pydantic AI validates the outputs of tools like `get_job` against your defined models, failing loudly if the structure breaks.

The framework supports both Streamable HTTP and SSE transports. You configure the `MCPToolset` to use the appropriate transport based on your deployment environment.

Yes. Pydantic AI is model-agnostic. You pull sensitive records using `list_technicians` and process them entirely through a local Llama instance to avoid sending employee data to third parties.

These tools expose raw price lists and customer phone numbers. Vinkius isolates the connection in a dedicated sandbox that processes the request and immediately terminates. We never cache your API responses on our infrastructure.

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