How to Use the Wenjuanxing / 问卷星 MCP in Pydantic AI

Q: How does Wenjuanxing / 问卷星 work with Pydantic AI?

The MCP Server exposes all tools—from getsurvey to listgroups—which your agent calls. The Pydantic framework then takes the raw API response and validates it against your defined Python models.

Q: Can I reliably update a Wenjuanxing / 问卷星 survey status using Pydantic AI?

Yes, because of type validation. The agent calls updatesurveystatus, and if the API response doesn't confirm success with the right structure, your program fails loudly—you know exactly what went wrong.

Q: What data types can I retrieve for reporting with Wenjuanxing / 问卷星 using Pydantic AI?

You get highly reliable structured objects from getreport and raw records from listresponses. The framework ensures that the fields in those reports match your defined schema every time.

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

The server handles account metadata via getaccountinfo, and it manages raw user inputs through the listresponses tool. Data integrity is its primary focus.

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Connect Wenjuanxing / 问卷星 MCP to Pydantic AI

Create your Vinkius account to connect Wenjuanxing / 问卷星 to Pydantic AI 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 Pydantic AI

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Guaranteed Survey Creation

The agent ensures that every survey created meets strict type requirements. When you call `create_survey`, the output is validated against Pydantic models, so you never get a malformed questionnaire object. This means your Python code gets reliable data back from tools like `get_account_info` and can proceed without runtime surprises.

Correct Data Extraction

Pydantic AI enforces correct data when retrieving survey results. Whether you use `get_report` or list all submissions with `list_responses`, the returned JSON structure is guaranteed to match your expectations. This focus on correctness means you can trust that stats from `get_stats` are formatted exactly how your application needs them.

Type-Safe Status Changes

When managing a survey's lifecycle, Pydantic AI validates the status inputs. Calling `update_survey_status` requires specific parameters—the framework catches errors if you pass bad data. It also ensures that when listing assets using `list_surveys`, the returned objects have consistent and correct fields.

Setup guide

Set up Wenjuanxing / 问卷星 MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "wenjuanxing-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Wenjuanxing / 问卷星 tools.",
)

result = await agent.run("List recent Wenjuanxing / 问卷星 transactions")
print(result.output)

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Common questions about Wenjuanxing / 问卷星 MCP in Pydantic AI

The MCP Server exposes all tools—from `get_survey` to `list_groups`—which your agent calls. The Pydantic framework then takes the raw API response and validates it against your defined Python models.

Yes, because of type validation. The agent calls `update_survey_status`, and if the API response doesn't confirm success with the right structure, your program fails loudly—you know exactly what went wrong.

You get highly reliable structured objects from `get_report` and raw records from `list_responses`. The framework ensures that the fields in those reports match your defined schema every time.

Since it's model-agnostic and focused on output validation, it supports any chain of calls. You can first `list_surveys` and then iterate through the list to call `get_stats` on each one.

The server handles account metadata via `get_account_info`, and it manages raw user inputs through the `list_responses` tool. Data integrity is its primary focus.

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