How to Use the Gingr MCP in OpenAI Agents SDK

Q: How do I connect the Gingr database to my OpenAI Agents SDK pipeline?

Install the openai-agents package and configure the MCPServerStreamableHttp client with your Vinkius endpoint. Pass this server instance directly to your agent constructor to enable automatic tool discovery.

Q: Can my OpenAI Agents SDK setup update the physical Gingr whiteboard?

No, the getdigitalwhiteboard tool only reads the current digital whiteboard status to assist your front-desk staff. It doesn't write back to Gingr, keeping your physical operations safe from automated writing errors.

Q: How does the Gingr MCP Server handle multi-location facilities in OpenAI Agents SDK?

The listbusinesslocations tool allows your OpenAI agents to identify which specific facility a pet is checked into. This prevents staff at one kennel location from pulling records belonging to another facility.

Q: How do I verify that my OpenAI agent can reach the database?

You run the verifyapiconnection tool within your agent's initialization routine. This returns a direct connection status check, confirming your API token is active before your agents start processing owner queries.

Run secure pet care operations by connecting Gingr to your OpenAI Agents SDK with built-in guardrails and automatic tool execution.

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Connect Gingr MCP to OpenAI Agents SDK

Create your Vinkius account to connect Gingr to OpenAI Agents SDK 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 OpenAI Agents SDK

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Track live boarding status using OpenAI Agents SDK

The `list_active_checkins` tool pulls real-time kennel occupancy data directly into your agent's run loop. Your OpenAI agents use this live data to verify which dogs are currently on-site before executing any automated tasks. This setup prevents scheduling conflicts by cross-referencing active check-ins against existing bookings. By routing this data through the MCP Server endpoint, you get clean tracing of every single database query your agent runs.

Validate owner profiles with native agent guardrails

The `get_pet_owner_details` tool retrieves complete customer records, including contact details and custom account flags. The OpenAI Agents SDK lets you set strict validation rules so your agents don't send messages to the wrong phone number. If an agent tries to pull an owner profile without a verified ID, the SDK's built-in guardrail halts execution instantly. This ensures your staff only see accurate, pre-screened customer data during peak morning drop-off hours.

Secure custom pet data lookup via MCP Server tools

The `search_pet_custom_fields` tool scans your Gingr database for critical behavioral alerts and medical requirements. Your OpenAI agents read these custom fields to flag aggressive dogs or special dietary needs before kennel placement. Using the SDK's multi-agent handoff pattern, a triage agent can instantly pass complex medical alerts to a senior staff-assist agent. This keeps your physical facility safe while automating routine data lookups.

Setup guide

Set up Gingr MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Gingr tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Gingr tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Gingr tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Gingr Agent",
            instructions="You have access to Gingr tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Gingr. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Gingr MCP in OpenAI Agents SDK

Install the `openai-agents` package and configure the `MCPServerStreamableHttp` client with your Vinkius endpoint. Pass this server instance directly to your agent constructor to enable automatic tool discovery.

No, the `get_digital_whiteboard` tool only reads the current digital whiteboard status to assist your front-desk staff. It doesn't write back to Gingr, keeping your physical operations safe from automated writing errors.

The `list_business_locations` tool allows your OpenAI agents to identify which specific facility a pet is checked into. This prevents staff at one kennel location from pulling records belonging to another facility.

You run the `verify_api_connection` tool within your agent's initialization routine. This returns a direct connection status check, confirming your API token is active before your agents start processing owner queries.

The Vinkius MCP Server handles your API credentials inside isolated V8 sandboxes, meaning your raw keys aren't ever exposed to the OpenAI Agents SDK. Only the specific JSON payloads returned by tools like `find_owner_by_phone` are transmitted over the secure endpoint.

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