Where do I find my Nicereply API Key?

Log in to Nicereply, go to Settings > API and Integrations, and you will find your 'Private API Key' there.

Does this support NPS surveys?

Yes! Nicereply supports CSAT, CES, and NPS. You can list all your surveys using the `list_surveys` tool and get stats for any of them.

Can I see feedback for a specific support agent?

You can use the `list_users` tool to find the Agent ID and then filter responses or view individual ratings recorded in the platform.

Nicereply MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Nicereply
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Nicereply 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 Nicereply "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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About Nicereply MCP Server

Connect your Nicereply account to your AI agent and gain deep insights into your customer satisfaction and agent performance through natural conversation.

Pydantic AI validates every Nicereply tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Response Monitoring — List and inspect all customer satisfaction ratings and feedback responses in real-time.
Survey Analytics — Access CSAT, CES, and NPS surveys and retrieve detailed performance metrics and statistics.
Agent Performance — List workspace users and monitor their individual ratings and feedback scores.
Customer Insights — View customer profiles and their historical feedback patterns.
Rating Standards — Retrieve the definitions of rating values and scales used across your surveys.
Deep Inspection — Fetch complete metadata for specific responses or surveys using their unique IDs.

The Nicereply MCP Server exposes 10 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 Nicereply to Pydantic AI via MCP

Follow these steps to integrate the Nicereply 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 10 tools from Nicereply with type-safe schemas

Why Use Pydantic AI with the Nicereply MCP Server

Pydantic AI provides unique advantages when paired with Nicereply 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 Nicereply integration code

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

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

Nicereply + Pydantic AI Use Cases

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

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

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

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

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

Nicereply MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Nicereply to Pydantic AI via MCP:

get_customer

Get specific customer details

get_me

Get current user details

get_rating_values

List possible rating values

get_response

Get specific response details

get_survey

Get specific survey details

get_survey_stats

Get survey statistics

list_customers

List Nicereply customers

list_responses

List feedback responses

list_surveys

List all surveys

list_users

List workspace users (agents)

Example Prompts for Nicereply in Pydantic AI

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

"Show me the latest customer feedback responses."

"What is the current performance of our CSAT survey?"

"List all active surveys in my Nicereply account."

Troubleshooting Nicereply MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Nicereply + Pydantic AI FAQ

Common questions about integrating Nicereply 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 Nicereply MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.