How to Use the TurfHop MCP in Pydantic AI

Q: How does TurfHop manage job creation using Pydantic AI?

You use the createjob tool. The agent passes the required job details as a JSON string, and before the MCP Server executes, Pydantic AI validates that all fields are present and correctly typed.

Q: What is the process for listing customers with TurfHop and Pydantic AI?

The listcustomers tool retrieves client records. Because of Pydantic AI's validation, you know exactly what format the resulting customer list will be in—no weird fields to worry about.

Q: Can I update billing information using TurfHop and Pydantic AI?

Absolutely. You can use updatecustomer or updatejob. The system checks the data against your models, meaning if you try to send a date as a string when it expects a timestamp, the process fails immediately.

Q: What kind of products does TurfHop support with Pydantic AI?

The listproducts tool exposes your full catalog. This means whether you're listing mulch or specialized equipment, the data type received by your agent will be predictable and safe.

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Connect TurfHop MCP to Pydantic AI

Create your Vinkius account to connect TurfHop 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 TurfHop with Pydantic AI

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Create New Customer Records Safely

Need to onboard a client? Your agent calls `create_customer` by passing the data as a JSON string. Because this MCP Server is connected via Pydantic AI, any unexpected field in that JSON will fail loudly with an explicit validation error. This level of type safety means you never get silent corruption when adding new clients to your system.

Manage and Modify Service Jobs

The agent handles the job lifecycle using `create_job` or modifying existing work with `update_job`. Every time a tool is called, Pydantic AI validates that the payload structure matches the defined schema. This robustness makes it perfect for high-stakes operations where accuracy matters more than speed.

Retrieve Financial and Product Data

You can pull all historical financial data using `list_invoices` or check a specific bill with `get_invoice`. Similarly, calling `list_products` guarantees that the returned list adheres to strict data types. This reliability is key when building production systems with Pydantic AI.

Setup guide

Set up TurfHop 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": {
        "turfhop-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about TurfHop MCP in Pydantic AI

You use the `create_job` tool. The agent passes the required job details as a JSON string, and before the MCP Server executes, Pydantic AI validates that all fields are present and correctly typed.

The `list_customers` tool retrieves client records. Because of Pydantic AI's validation, you know exactly what format the resulting customer list will be in—no weird fields to worry about.

Absolutely. You can use `update_customer` or `update_job`. The system checks the data against your models, meaning if you try to send a date as a string when it expects a timestamp, the process fails immediately.

The `list_products` tool exposes your full catalog. This means whether you're listing mulch or specialized equipment, the data type received by your agent will be predictable and safe.

This MCP Server handles invoice details. The use of Pydantic AI ensures that all financial fields—like amounts due or payment dates—are correctly typed and validated before your agent processes them.

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