How to Use the QuestionPro MCP in Pydantic AI

Q: How do I configure the QuestionPro MCP Server with Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your server's HTTP endpoint. Pass the toolset directly into your Agent constructor to give it access to all survey tools.

Q: Can I use Pydantic AI to manage QuestionPro email lists?

Yes, your agent can call listemaillists to retrieve your contact databases. You can define a Pydantic model to ensure the returned email list data matches your required schema.

Q: How does the QuestionPro MCP Server protect survey responses in Pydantic AI?

All survey responses fetched by listresponses are validated in your local memory space. Vinkius runs the server in an isolated, zero-trust sandbox, ensuring that your API keys and survey data remain strictly confidential.

Run type-safe QuestionPro data extraction with Pydantic AI to guarantee valid survey schemas at runtime.

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Connect QuestionPro MCP to Pydantic AI

Create your Vinkius account to connect QuestionPro to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup QuestionPro with Pydantic AI

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Validate QuestionPro responses using Pydantic AI

Stop worrying about corrupted survey data breaking your code. Your agent calls `get_response` and validates the incoming JSON payload against your strict Pydantic schemas. If a respondent leaves a field blank or enters unexpected data, Pydantic AI catches it instantly. This ensures your downstream analytics engine only receives clean, validated responses.

Map QuestionPro questions via the MCP Server

When your agent builds a dynamic report, it needs to know the exact question structures. The agent calls `list_questions` and `get_question` to inspect the survey format. This MCP Server integration ensures that every question type, from multiple-choice to open text, is parsed into structured Python types. No more silent formatting errors in your reports.

Verify survey health and statistics

Ensure your data pipeline only runs on active, healthy surveys. The agent executes `check_questionpro_status` and `get_survey_stats` to verify the API connection and check completion rates. If the survey statistics do not meet your validation thresholds, the agent raises an error immediately. This prevents your system from running expensive analysis on incomplete datasets.

Setup guide

Set up QuestionPro 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": {
        "questionpro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent QuestionPro transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by QuestionPro. 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 QuestionPro MCP in Pydantic AI

Install the package using pip install "pydantic-ai-slim[mcp]" and initialize MCPToolset with your server's HTTP endpoint. Pass the toolset directly into your Agent constructor to give it access to all survey tools.

The framework will raise a validation error immediately. Because Pydantic AI validates every payload returned by tools like get_response against runtime types, you prevent corrupted data from entering your database.

Yes, your agent can call list_email_lists to retrieve your contact databases. You can define a Pydantic model to ensure the returned email list data matches your required schema.

Yes, the framework is completely model-agnostic. You can run the server alongside local models or commercial APIs while using tools like list_surveys to fetch data.

All survey responses fetched by list_responses are validated in your local memory space. Vinkius runs the server in an isolated, zero-trust sandbox, ensuring that your API keys and survey data remain strictly confidential.

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