Wenjuanxing / 问卷星 MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Wenjuanxing / 问卷星 through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Wenjuanxing / 问卷星 Assistant",
instructions=(
"You help users interact with Wenjuanxing / 问卷星. "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Wenjuanxing / 问卷星"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Wenjuanxing / 问卷星 MCP Server
Empower your AI agent to orchestrate your data collection and research with Wenjuanxing (WJX), the premier online survey platform in China. By connecting Wenjuanxing to your agent, you transform complex questionnaire management, response auditing, and data analysis into a natural conversation. Your agent can instantly list your surveys, retrieve detailed structure and metadata, monitor real-time response counts, and even generate high-level analysis reports without you ever needing to navigate the comprehensive web interface. Whether you are conducting market research or auditing employee engagement, your agent acts as a real-time research assistant, keeping your data accurate and your insights moving.
The OpenAI Agents SDK auto-discovers all 10 tools from Wenjuanxing / 问卷星 through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Wenjuanxing / 问卷星, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Survey Orchestration — List all questionnaires and retrieve detailed structure and metadata for each.
- Response Monitoring — List and retrieve actual response data to monitor participation and engagement.
- Analytical Reporting — Retrieve high-level summary reports and quantitative statistics for survey results.
- Content Control — Create new survey structures and update the status of existing questionnaires.
- Organization Insights — Browse survey folders and retrieve metadata about your Wenjuanxing account.
The Wenjuanxing / 问卷星 MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Wenjuanxing / 问卷星 to OpenAI Agents SDK via MCP
Follow these steps to integrate the Wenjuanxing / 问卷星 MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from Wenjuanxing / 问卷星
Why Use OpenAI Agents SDK with the Wenjuanxing / 问卷星 MCP Server
OpenAI Agents SDK provides unique advantages when paired with Wenjuanxing / 问卷星 through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Wenjuanxing / 问卷星 + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Wenjuanxing / 问卷星 MCP Server delivers measurable value.
Automated workflows: build agents that query Wenjuanxing / 问卷星, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Wenjuanxing / 问卷星, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Wenjuanxing / 问卷星 tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Wenjuanxing / 问卷星 to resolve tickets, look up records, and update statuses without human intervention
Wenjuanxing / 问卷星 MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect Wenjuanxing / 问卷星 to OpenAI Agents SDK via MCP:
create_survey
Create a new questionnaire
get_account_info
Get user account metadata
get_report
Get survey summary report
get_stats
Get survey statistics
get_survey
Get questionnaire details
list_groups
List survey groups
list_responses
List survey responses
list_surveys
List questionnaires
query_surveys
Search questionnaires by keyword
update_survey_status
g., publish, pause) of a specific survey. Update survey status
Example Prompts for Wenjuanxing / 问卷星 in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Wenjuanxing / 问卷星 immediately.
"List all active surveys in my Wenjuanxing account."
"Show me the responses for survey activity '8821'."
"What are the statistics for questionnaire '9920'?"
Troubleshooting Wenjuanxing / 问卷星 MCP Server with OpenAI Agents SDK
Common issues when connecting Wenjuanxing / 问卷星 to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Wenjuanxing / 问卷星 + OpenAI Agents SDK FAQ
Common questions about integrating Wenjuanxing / 问卷星 MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Wenjuanxing / 问卷星 with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Wenjuanxing / 问卷星 to OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.
