How to Use the Loopio MCP in OpenAI Agents SDK

Automate your RFP responses inside OpenAI Agents SDK. Connect your library and manage submissions without leaving your Python environment.

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Connect Loopio MCP to OpenAI Agents SDK

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

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Instant knowledge retrieval for OpenAI Agents SDK

Stop digging through folders for outdated compliance answers. Your agent uses `search_library` to pull verified responses directly from your knowledge base. This keeps your drafts accurate while the `list_libraries` tool ensures the agent knows exactly which stacks are available for a specific project.

Manage RFP projects with an MCP Server

Your agent tracks progress across your entire workspace. It uses `list_projects` to identify active RFPs and `get_project` to check if a submission is ready for review. You gain full visibility into your pipeline without manual status updates or dashboard switching.

Automate questionnaire responses in Python

Speed up your response time by letting the agent handle the heavy lifting. It executes `list_questionnaires` to scope the work and `get_questionnaire_responses` to verify draft status before you finalize anything. Once the agent prepares the content, it uses `create_submission` to push the final output back into the system. This closes the loop between your agent and your team.

Setup guide

Set up Loopio 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 Loopio tools at runtime.
3

Create your Agent

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

Run the agent

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

Yes. The server provides direct access to your knowledge base. Your agent will use the search tools to find exact matches for incoming questions.

It does. You can trigger the creation of a new RFP project through your agent. It handles the metadata and initializes the submission record for you.

You should use a human-in-the-loop workflow. Have your agent draft the response using the library, then review the output before final submission.

The server respects your existing workspace setup. You can use the team member tools to confirm who is assigned to specific tasks.

All requests are authenticated via your endpoint token. Your proprietary compliance data stays within the defined boundaries of your connection.

Use it with your favorite AI tools

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