How to Use the Alphamoon MCP in OpenAI Agents SDK

Q: How do I connect the Alphamoon MCP Server to OpenAI Agents SDK?

Install the package with pip. Create an MCPServerStreamableHttp instance using your Vinkius endpoint. Pass it to your Agent constructor and the tools auto-discover instantly.

Q: Can I list specific document batches in OpenAI Agents SDK using Alphamoon?

Use the listdocumentsbyprocess tool. Your agent calls this to fetch every file tied to a specific extraction job.

Build production Python agents with OpenAI Agents SDK that read contracts and extract invoice data through Vinkius.

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Connect Alphamoon MCP to OpenAI Agents SDK

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

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Document processing with OpenAI Agents SDK

The `upload_document` tool pushes PDFs and images directly into the Alphamoon MCP Server extraction pipeline. Your agent triggers this action, and the OpenAI SDK guardrails validate the file format before execution. This prevents broken uploads from burning through your token limits. Once the file hits the server, `get_ocr_results` pulls the raw text back into your agent's context window. You can then route this text to specialized agents for contract analysis or invoice reconciliation. The OpenAI dashboard tracks every step of this handoff.

Extract structured data from forms

Calling `get_extraction_results` returns specific key-value pairs from your processed documents. The agent pulls totals, dates, and vendor names from invoices without writing custom regex. This data flows directly into your Python application logic. You manage the extraction logic using `list_templates` and `get_template`. These tools let your agent pick the right document model based on the file type. Switching templates happens automatically when the payload requires different processing rules.

Manage pipeline states natively

Agents track processing queues using `list_processes` and `get_process`. This gives your system real-time visibility into document batch status. The OpenAI tracing features log exactly when the agent checks the queue. Cleanup happens through the `delete_document` tool. Your agent removes sensitive files the moment extraction finishes. Passing the server to the constructor with `cacheToolsList=True` keeps these MCP operations fast during heavy production loads.

Setup guide

Set up Alphamoon 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 Alphamoon tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the package with pip. Create an `MCPServerStreamableHttp` instance using your Vinkius endpoint. Pass it to your Agent constructor and the tools auto-discover instantly.

The server handles contracts, forms, and invoices. You send the files via `upload_document` and the backend OCR pipeline takes over.

Yes. The SDK guardrails check the tool outputs before passing them back to the agent. This ensures your production system only acts on correctly formatted OCR results.

Use the `list_documents_by_process` tool. Your agent calls this to fetch every file tied to a specific extraction job.

Your financial documents process in an ephemeral V8 Isolate Sandbox. Vinkius drops the environment immediately after the OCR extraction finishes. Zero vendor retention occurs.

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