How to Use the Wenjuanxing / 问卷星 MCP in OpenAI Agents SDK
Connects to Wenjuanxing / 问卷星: Build reliable survey data pipelines with OpenAI Agents SDK.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Wenjuanxing / 问卷星 MCP to OpenAI Agents SDK
Create your Vinkius account to connect Wenjuanxing / 问卷星 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.
Manage the Survey Lifecycle
Your agent handles creating and managing questionnaires entirely. It lets you `create_survey` to build new forms, or use `list_surveys` and `query_surveys` to find existing ones by keyword. Need to change a form's availability? You can call `update_survey_status` to publish, pause, or take a questionnaire offline. This keeps your production agent workflow safe from accidental edits.
Extracting and Analyzing Responses
Getting the data is straightforward using Wenjuanxing / 问卷星's tools. You can list all responses via `list_responses`, or pull raw details by calling `get_survey` for a specific form. For quick overviews, your agent runs `get_report` to get the summary report, and uses `get_stats` when it needs hard numbers on participation rates. It makes data retrieval reliable.
Deep Account and Group Access
Beyond surveys, you can manage the organizational structure of your data. Use `list_groups` to see which internal groups are tied to the survey platform. Account visibility is handled by `get_account_info`, giving your agent metadata about the user running the workflow. This helps ensure that subsequent actions have proper context.
Set up Wenjuanxing / 问卷星 MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all Wenjuanxing / 问卷星 tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Wenjuanxing / 问卷星 tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Wenjuanxing / 问卷星 tools and returns structured results. Copy the full example on the right to get started.
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="Wenjuanxing / 问卷星 Agent",
instructions="You have access to Wenjuanxing / 问卷星 tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main()) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Wenjuanxing / 问卷星. 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 Wenjuanxing / 问卷星 MCP in OpenAI Agents SDK
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