How do I find my Survey Hash ID?

Use the `list_surveys` tool to see a comprehensive list of all surveys in your account along with their unique hash IDs.

Can I filter survey responses by date?

Yes, the `list_survey_answers` tool accepts optional `date_from` and `date_to` parameters in YYYY-MM-DD format.

Is it possible to programmatically submit an NPS score?

Absolutely. You can use the specific create tools (e.g., `create_link_answer`) to submit scores and comments for any survey channel.

Zenloop MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Zenloop
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Zenloop through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Zenloop "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Zenloop?"
    )
    print(result.data)

asyncio.run(main())

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* 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 Zenloop MCP Server

Connect your Zenloop account to any AI agent to streamline your Net Promoter System (NPS) and customer feedback management. This MCP server enables your agent to interact with surveys, responses (answers), and account metadata directly from natural language.

Pydantic AI validates every Zenloop tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Survey Oversight — List all your active and historical surveys and retrieve their detailed summaries
Feedback Extraction — List customer answers and responses for any survey, filtered by date range
Response Generation — Programmatically create new survey answers across Link, Email, and Website channels
Performance Monitoring — Access NPS scores and comments to track customer sentiment in real-time
Account Visibility — Retrieve high-level account configuration and metadata for your Zenloop project

The Zenloop MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI 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 Zenloop to Pydantic AI via MCP

Follow these steps to integrate the Zenloop MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Zenloop with type-safe schemas

Why Use Pydantic AI with the Zenloop MCP Server

Pydantic AI provides unique advantages when paired with Zenloop through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Zenloop integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Zenloop connection logic from agent behavior for testable, maintainable code

Zenloop + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Zenloop MCP Server delivers measurable value.

Type-safe data pipelines: query Zenloop with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Zenloop tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Zenloop and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Zenloop responses and write comprehensive agent tests

Zenloop MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Zenloop to Pydantic AI via MCP:

create_email_answer

Create a new survey response for an Email Embed channel

create_embed_answer

Create a new survey response for a Website Embed channel

create_link_answer

Create a new survey response for a Link channel

create_overlay_answer

Create a new survey response for a Website Overlay channel

get_account_details

Get Zenloop account information

get_survey_details

Get details for a specific survey

list_survey_answers

Can be filtered by date. List answers (responses) for a survey

list_surveys

List all configured surveys

Example Prompts for Zenloop in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Zenloop immediately.

"List all active surveys in my Zenloop account."

"Show me customer responses for survey ID 'abc123xyz' from last week."

"Submit a Link response for survey 'abc123' with score 10 and comment 'Amazing experience!'."

Troubleshooting Zenloop MCP Server with Pydantic AI

Common issues when connecting Zenloop to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zenloop + Pydantic AI FAQ

Common questions about integrating Zenloop MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Zenloop MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.