How to Use the GetFeedback MCP in OpenAI Agents SDK

Q: How do I connect GetFeedback to the OpenAI Agents SDK?

Install openai-agents via pip. Instantiate MCPServerStreamableHttp with your endpoint URL and pass it to the mcpservers array in your Agent constructor to initialize the MCP Server.

Q: Will caching improve my OpenAI Agents SDK response time with GetFeedback?

Setting cacheToolsList=True prevents the agent from re-fetching tool schemas on every run. This speeds up calls to endpoints like getsurveydetails.

Q: How do I handle pagination for large datasets?

Your agent automatically loops through listfeedbackpage when pulling massive response logs. The SDK handles the asynchronous execution natively.

Connect GetFeedback to the OpenAI Agents SDK to automate survey distribution and track customer sentiment in production environments.

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Connect GetFeedback MCP to OpenAI Agents SDK

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

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Trigger Surveys with the MCP Server

The GetFeedback MCP Server lets your OpenAI agents dispatch emails directly to customers using `send_survey_invites`. You set the safety constraints before execution. If an agent tries to blast a list without checking rate limits, the framework stops it. Built-in tracing through the OpenAI dashboard tracks exactly when `verify_api_connection` fires. You know which specialized agent handled the handoff and exactly what payload went to the external service.

Monitor Responses in Real Time

Your specialized agents pull live GetFeedback data using `list_recent_feedback` to catch negative sentiment fast. They hand off angry responses to a dedicated recovery agent. That agent then uses `get_response_details` to pull the full context before drafting an apology. Rate limits won't crash your pipeline. The agent automatically runs `check_api_limits` to ensure it has enough quota to handle the incoming volume.

Aggregate Sentiment Pipelines

Analytics agents use `get_survey_stats` to build daily executive summaries from your GetFeedback account. Instead of manually pulling reports, your system queries the API, formats the metrics, and pushes the result to Slack. When you need historical context, the agent calls `list_completed_feedback` to filter out partial data. It relies on MCP auto-discovery to find the right tool for the job without any manual mapping.

Setup guide

Set up GetFeedback 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 GetFeedback tools at runtime.
3

Create your Agent

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

Run the agent

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

Install `openai-agents` via pip. Instantiate `MCPServerStreamableHttp` with your endpoint URL and pass it to the `mcp_servers` array in your Agent constructor to initialize the MCP Server.

Yes. You enforce access controls using the framework's guardrails. The agent can only call `list_surveys` for the specific IDs you whitelist in your safety constraints.

Setting `cacheToolsList=True` prevents the agent from re-fetching tool schemas on every run. This speeds up calls to endpoints like `get_survey_details`.

Your agent automatically loops through `list_feedback_page` when pulling massive response logs. The SDK handles the asynchronous execution natively.

Customer email addresses and raw survey feedback remain within your isolated V8 sandbox. The zero-trust architecture ensures that when `get_my_identity` runs, auth tokens are never logged or exposed to the underlying LLM.

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