How to Use the Megaventory MCP in OpenAI Agents SDK

Q: Is caching supported for Megaventory tool definitions in this SDK?

Yes, you can set cacheToolsList=True inside your MCPServerStreamableHttpParams configuration. This prevents the OpenAI Agents SDK from fetching the list of Megaventory tools on every single request, saving network overhead.

Run secure, multi-agent inventory operations using the Megaventory MCP Server with OpenAI Agents SDK.

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Connect Megaventory MCP to OpenAI Agents SDK

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

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Enforce guardrails on stock checks

The `get_product_stock` tool pulls real-time quantities for any SKU across your warehouse locations. Registering this tool with your OpenAI Agents SDK setup lets you set strict pre-execution guardrails to prevent unauthorized stock lookups or rate-limit overruns. You configure the agent using `MCPServerStreamableHttp` to connect directly to the Megaventory MCP Server. This setup ensures that every stock inquiry complies with your custom guardrails before execution, keeping your inventory queries secure.

Route Megaventory orders via agent handoffs

Your procurement agent uses the `get_purchase_order` tool to inspect incoming supplier shipments, while a separate sales agent monitors orders via `get_sales_order`. The OpenAI Agents SDK hands off tasks between these specialized agents when a sales order depends on a pending purchase. You pass the Megaventory server instance inside the `mcp_servers` list during agent initialization. The SDK automatically registers the tools so your agents can dynamically trade off tasks without manual routing code.

Trace Megaventory MCP Server tools in OpenAI dashboard

The `search_products` tool queries your Megaventory catalog using simple text descriptions to match SKUs. When your agent runs this search, the OpenAI Agents SDK sends full execution traces to your developer dashboard so you can monitor tool latency and input parameters. Setting `cacheToolsList=True` in your Python setup speeds up these searches by caching the tool schema. You see exactly how the agent translates natural language queries into structured `search_products` calls, making debugging straightforward.

Setup guide

Set up Megaventory 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 Megaventory tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Megaventory 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="Megaventory Agent",
            instructions="You have access to Megaventory 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 Megaventory MCP in OpenAI Agents SDK

Install the SDK with pip and initialize the connection using `MCPServerStreamableHttp`. You pass the HTTP endpoint of this Megaventory MCP server directly into your agent's `mcp_servers` configuration block. The agent then automatically discovers all ten tools.

Yes, you control tool access by defining specific system prompts and guardrails inside your OpenAI agent definition. This prevents the agent from running write operations like purchase order changes if it only needs `get_product_stock` for basic queries.

The SDK relies on your application's middleware or the guardrails you define in your agent's execution loop. If a tool like `list_products` hits Megaventory's rate limit, your agent can catch the error and retry using backoff strategies.

Yes, you can set `cacheToolsList=True` inside your `MCPServerStreamableHttpParams` configuration. This prevents the OpenAI Agents SDK from fetching the list of Megaventory tools on every single request, saving network overhead.

This MCP server processes your Megaventory sales orders, purchase orders, and stock levels in a zero-trust V8 sandbox on Vinkius. Your API keys are encrypted at rest and injected only at runtime, ensuring that no raw transaction data is stored or leaked during tool execution.

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