How to Use the Wenjuanxing / 问卷星 MCP in Google ADK

Q: Can I use the Google ADK to change Wenjuanxing / 问卷星 survey status?

Yes. You pass the toolset to the LlmAgent, which executes updatesurveystatus. This lets you programmatically publish or pause any survey questionnaire.

Q: What data types does Wenjuanxing / 问卷星 provide for reporting with Google ADK?

You get both summary reports via getreport and lists of raw responses using listresponses. Since the agent runs on Google Cloud, this data is ready to be held in BigQuery.

Q: What sensitive data types does this Wenjuanxing / 问卷星 MCP Server touch?

The server accesses account metadata via getaccountinfo, along with survey response content from the listresponses tool. This is key for auditing.

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Connect Wenjuanxing / 问卷星 MCP to Google ADK

Create your Vinkius account to connect Wenjuanxing / 问卷星 to Google ADK 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 Wenjuanxing / 问卷星 with Google ADK

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Survey Creation and Management

You use the Google ADK to manage the entire form lifecycle. The agent can `create_survey` when you need a new questionnaire, or it can search existing assets using `query_surveys`. This keeps your data pipeline clean. Need to control visibility? You call `update_survey_status`, making sure the survey is live only when it's ready for responses.

Advanced Data Retrieval and Reporting

The agent pulls deep insights from Wenjuanxing / 问卷星 data. It retrieves summary reports using `get_report` or gets detailed metrics via `get_stats`. These structured results are perfect for feeding directly into BigQuery. When you need to see what people actually submitted, the agent uses `list_responses`, giving you access to every raw submission record.

Systematic Asset Listing

The Google ADK lets your agent systematically list and find assets. Use `list_surveys` or `list_groups` to map out all related components in the platform. For deep dives, you can use `get_survey` to pull all metadata for a specific questionnaire, giving your enterprise application complete context.

Setup guide

Set up Wenjuanxing / 问卷星 MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Wenjuanxing / 问卷星 tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Wenjuanxing / 问卷星_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Wenjuanxing / 问卷星 tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Wenjuanxing / 问卷星 MCP in Google ADK

The Google ADK integrates directly into Gemini models running on Vertex AI. Your agent uses this connection to perform actions like `get_stats` and `list_responses`, making sure the data flows right into your BigQuery tables.

Yes. You pass the toolset to the LlmAgent, which executes `update_survey_status`. This lets you programmatically publish or pause any survey questionnaire.

You get both summary reports via `get_report` and lists of raw responses using `list_responses`. Since the agent runs on Google Cloud, this data is ready to be held in BigQuery.

It supports multi-step workflows. For example, your agent can first `query_surveys` for a group of forms and then loop through them to call `get_stats` on each one.

The server accesses account metadata via `get_account_info`, along with survey response content from the `list_responses` tool. This is key for auditing.

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