How to Use the Zenloop MCP in Pydantic AI

Q: How does Pydantic AI handle NPS feedback with Zenloop?

Zenloop provides the raw survey answers via tools like listsurveyanswers. The strength here is that every single response record passes through a validator, ensuring the data your agent uses for analysis is clean.

Q: Can I list all surveys using Zenloop and Pydantic AI?

Yes. Calling listsurveys provides an accurate, type-safe list of every configured survey. The strict validation means you don't have to worry about structural changes breaking your agent.

Q: What if I need to create a new response using Zenloop and Pydantic AI?

You can use any creation tool—createlinkanswer, createoverlayanswer, etc. The framework validates the output, so even if the source data is messy, the record sent back is guaranteed correct.

Q: Is filtering available when listing results with Zenloop and Pydantic AI?

Absolutely. When you call listsurveyanswers, you can filter by date. This focused data stream helps your agent process only the necessary records, maintaining high integrity.

Guarantee data integrity when managing customer surveys with Pydantic AI.

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Connect Zenloop MCP to Pydantic AI

Create your Vinkius account to connect Zenloop 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 Zenloop with Pydantic AI

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Creating Survey Records

To record feedback, your agent uses tools like `create_email_answer`, `create_embed_answer`, and the others. Because this MCP Server validates against Pydantic models, you'll fail loudly if the API data doesn't match expected schemas. This means every new survey response—regardless of whether it came from a website overlay or an email embed—is type-checked before your agent proceeds.

Viewing Survey Data

Want to see what surveys Zenloop is running? Call `list_surveys` to get the master list. The resulting data structure is guaranteed to conform to Pydantic models at runtime. For deeper dives, use `get_survey_details`. This tool provides full survey metadata that passes through strict validation, ensuring your agent never encounters unexpected or corrupted fields.

Checking Account Status

Your first step should be calling `get_account_details`. The Pydantic AI framework validates the returned Zenloop account information instantly. You know that if it returns, it's correct. This prevents your agent from making assumptions about credentials or structure based on bad API responses.

Setup guide

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

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

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

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Common questions about Zenloop MCP in Pydantic AI

Zenloop provides the raw survey answers via tools like `list_survey_answers`. The strength here is that every single response record passes through a validator, ensuring the data your agent uses for analysis is clean.

Yes. Calling `list_surveys` provides an accurate, type-safe list of every configured survey. The strict validation means you don't have to worry about structural changes breaking your agent.

You can use any creation tool—`create_link_answer`, `create_overlay_answer`, etc. The framework validates the output, so even if the source data is messy, the record sent back is guaranteed correct.

Absolutely. When you call `list_survey_answers`, you can filter by date. This focused data stream helps your agent process only the necessary records, maintaining high integrity.

The MCP Server handles survey answers and account details. It specifically deals with NPS feedback and structured survey answer records, all of which are rigorously type-checked by the framework.

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