How to Use the Gingr MCP in Pydantic AI

Q: How do I initialize the Gingr toolset in Pydantic AI?

Install pydantic-ai-slimmcp and use the MCPToolset class to connect to your Vinkius HTTP endpoint. Pass this toolset instance inside the toolsets list parameter when defining your Pydantic AI agent.

Q: How do I verify my database credentials in Pydantic AI?

You run the verifyapiconnection tool during your application's startup phase. This confirms that your Vinkius endpoint can communicate with your database before the agent accepts any user queries.

Q: How does this setup protect custom owner notes from leaking?

Data retrieved via getpetownerdetails is validated locally in your runtime environment. Our MCP Server operates under a zero-trust architecture, ensuring that your sensitive owner notes and contact details are processed in ephemeral sandboxes and never cached.

Validate every pet record and booking detail at runtime using type-safe Pydantic AI agents connected to Gingr.

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Connect Gingr MCP to Pydantic AI

Create your Vinkius account to connect Gingr 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 Gingr with Pydantic AI

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Enforce strict type safety on active check-in data

The `list_active_checkins` tool delivers real-time kennel occupancy data directly to your Pydantic AI agent. The framework validates this incoming payload against strict Python schemas at runtime to prevent malformed records. By validating through the MCP Server, your agent fails loudly instead of processing corrupted check-in logs. This runtime validation ensures your staff won't act on incomplete or broken pet records.

Parse custom pet fields with zero silent failures

The `search_pet_custom_fields` tool retrieves behavioral and medical alerts from your Gingr database. Pydantic AI maps these custom fields to typed Python models, ensuring critical alerts are never missed due to parsing errors. This strict verification prevents your agent from hallucinating or misinterpreting unstructured text fields. Your staff get reliable, validated warnings about aggressive dogs or strict medication schedules before handling any pet.

Secure owner lookups via a type-safe MCP Server

The `find_owner_by_email` tool queries your customer database and returns verified owner profiles to Pydantic AI. The framework checks that email formats, phone numbers, and IDs match your exact model requirements before passing them to the LLM. By using this MCP Server, you eliminate the risk of your agent hallucinating customer details during busy morning rushes. Your front-desk staff receive clean, verified owner profiles every single time.

Setup guide

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

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

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

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Common questions about Gingr MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use the `MCPToolset` class to connect to your Vinkius HTTP endpoint. Pass this toolset instance inside the `toolsets` list parameter when defining your Pydantic AI agent.

The framework will attempt to parse the custom data into your defined schema. If the payload from `search_owner_custom_fields` fails validation, the agent raises a validation error immediately, preventing incorrect data from reaching your staff.

Yes, Pydantic AI is model-agnostic, meaning you can run your validated pet-care agents on local models or commercial APIs. The underlying tools, like `list_owner_reservations`, will operate identically regardless of your chosen model.

You run the `verify_api_connection` tool during your application's startup phase. This confirms that your Vinkius endpoint can communicate with your database before the agent accepts any user queries.

Data retrieved via `get_pet_owner_details` is validated locally in your runtime environment. Our MCP Server operates under a zero-trust architecture, ensuring that your sensitive owner notes and contact details are processed in ephemeral sandboxes and never cached.

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