How to Use the Alchemer MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK logic trigger Alchemer reports?

Yes, you can call listsurveyreports directly from your agent. This lets your system fetch generated insights whenever the agent logic demands them.

Connect Alchemer to your OpenAI Agents SDK backend to pull survey data directly into your production agent logic.

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Connect Alchemer MCP to OpenAI Agents SDK

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

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Survey data access via OpenAI Agents SDK

Stop manual exports by using `list_surveys` and `list_survey_responses` to feed raw feedback into your agent memory. The tools map directly to your Python environment for immediate processing. Your agent handles the data flow within your defined safety guardrails. Use `get_response_details` to isolate specific participant feedback without leaving your code.

Manage campaigns and contacts

Automate your outreach by calling `list_survey_campaigns` to track distribution status across your user base. Your agent keeps tabs on active projects without writing custom scripts. Pull lists of participants with `list_contact_lists` to verify audience reach. This MCP server lets your agent manage the logistics of feedback collection in real-time.

Deep metadata inspection

Inspect survey architecture using `list_survey_questions` to understand the structure of your incoming data. You get the full schema of your feedback forms. Use `get_question_details` to parse specific metadata and inform how your agent interprets user sentiment. It turns static survey forms into dynamic inputs for your logic.

Setup guide

Set up Alchemer 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 Alchemer tools at runtime.
3

Create your Agent

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

Run the agent

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

You initialize the MCPServerStreamableHttp class with your Vinkius endpoint. Pass this object into your agent constructor to make the tools available.

Yes, you can call `list_survey_reports` directly from your agent. This lets your system fetch generated insights whenever the agent logic demands them.

It does. Set cacheToolsList to True in your config to speed up discovery. This keeps your agent responsive during complex feedback analysis tasks.

The server exposes standard tool outputs that your agent interprets. You should wrap your calls in standard try-except blocks to manage network or API interruptions.

Vinkius runs the server in an isolated sandbox with zero-trust architecture. Only your specific endpoint token grants access to your survey submission logs.

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