How to Use the MoeGo MCP in OpenAI Agents SDK

Q: How do I connect the MoeGo MCP Server to my OpenAI Agents SDK pipeline?

Install the package using pip install openai-agents and initialize the server stream. Pass the server configuration to the Agent constructor using mcpservers=server to let the agent auto-discover tools like listappointments and createappointment.

Q: Can I cache the tool list in OpenAI Agents SDK when querying MoeGo?

Yes, set cacheToolsList=True in your agent setup. This prevents the agent from making redundant roundtrips to retrieve the tool schema, making calls to getpet or listservices significantly faster.

Q: How do guardrails in OpenAI Agents SDK protect my MoeGo scheduling data?

The SDK validates the parameters for tools like createappointment against the JSON schema before execution. This prevents the agent from writing broken dates or assigning non-existent staff IDs to a grooming booking.

Q: Can I run multi-agent handoffs for customer reviews?

Yes. You can build one agent that reads reviews via listreviews and another that pulls customer profiles with listcustomers. The SDK handles the handoff when an unhappy customer needs their profile updated or a follow-up scheduled.

Q: How does this setup secure my sensitive pet records?

All calls to listpets and customer contact details run in an ephemeral sandbox environment. This MCP setup ensures your API keys are never exposed to the LLM, and the platform handles the authorization token securely, ensuring pet and customer data never leaks.

Run your pet grooming business on autopilot using the OpenAI Agents SDK to manage appointments, staff, and customer records.

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Connect MoeGo MCP to OpenAI Agents SDK

Create your Vinkius account to connect MoeGo 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 MoeGo with OpenAI Agents SDK

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Auto-discover grooming tools with OpenAI Agents SDK

The `list_appointments` tool exposes your entire grooming schedule directly to your OpenAI agents using this MCP Server. Passing the server configuration block to the agent constructor maps out every route needed to check bookings or fetch pet profiles without manual route definition. Setting `cacheToolsList=True` ensures your agent does not waste latency querying the server schema on every message. Your scheduling stays fast when clients call to confirm times or when the agent needs to check `get_business_settings` to verify opening hours.

Secure booking workflows with built-in guardrails

The `create_appointment` tool lets your agent write new bookings directly into your database after verifying slot availability. Because the OpenAI Agents SDK forces strict schema validation before running tools, you do not have to worry about the agent sending malformed dates or invalid staff IDs to the MoeGo API. If a customer requests a specific groomer, the agent uses `list_staff` to find active employees and matches them against the selected service from `list_services`. The SDK guardrail blocks the API call immediately if the agent tries to book a service that does not exist.

Multi-agent handoffs for custom pet care pipelines

The `list_reviews` tool monitors customer satisfaction across all pet care visits. This MoeGo MCP Server allows you to split operations between specialized agents, like a booking agent and a customer feedback agent. When a bad review comes in, the feedback agent can automatically hand off the conversation to the scheduling agent. That agent then uses `get_appointment` to look up the exact visit details and offers a resolution, keeping your operations organized without a single monolithic prompt.

Setup guide

Set up MoeGo 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 MoeGo tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate MoeGo 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="MoeGo Agent",
            instructions="You have access to MoeGo tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about MoeGo MCP in OpenAI Agents SDK

Install the package using `pip install openai-agents` and initialize the server stream. Pass the server configuration to the `Agent` constructor using `mcp_servers=[server]` to let the agent auto-discover tools like `list_appointments` and `create_appointment`.

Yes, set `cacheToolsList=True` in your agent setup. This prevents the agent from making redundant roundtrips to retrieve the tool schema, making calls to `get_pet` or `list_services` significantly faster.

The SDK validates the parameters for tools like `create_appointment` against the JSON schema before execution. This prevents the agent from writing broken dates or assigning non-existent staff IDs to a grooming booking.

Yes. You can build one agent that reads reviews via `list_reviews` and another that pulls customer profiles with `list_customers`. The SDK handles the handoff when an unhappy customer needs their profile updated or a follow-up scheduled.

All calls to `list_pets` and customer contact details run in an ephemeral sandbox environment. This MCP setup ensures your API keys are never exposed to the LLM, and the platform handles the authorization token securely, ensuring pet and customer data never leaks.

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