Delighted MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Delighted through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Delighted "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Delighted?"
)
print(result.data)
asyncio.run(main())
* 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 Delighted MCP Server
Integrate Delighted by Qualtrics, the leading experience management platform, directly into your AI workflow. Monitor your customer feedback in real-time, track Net Promoter Score (NPS) metrics, and analyze survey comments using natural language.
Pydantic AI validates every Delighted tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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
- Feedback Monitoring — List and retrieve detailed survey responses, including scores and text comments from your customers.
- Metric Intelligence — Retrieve overall NPS metrics, including promoter, passive, and detractor counts.
- Customer Research — Access feedback history and metadata for specific individuals in your database.
- Survey Automation — Add new people to Delighted to trigger feedback surveys directly via chat.
The Delighted 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 Delighted to Pydantic AI via MCP
Follow these steps to integrate the Delighted 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 Delighted with type-safe schemas
Why Use Pydantic AI with the Delighted MCP Server
Pydantic AI provides unique advantages when paired with Delighted 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 Delighted integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Delighted connection logic from agent behavior for testable, maintainable code
Delighted + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Delighted MCP Server delivers measurable value.
Type-safe data pipelines: query Delighted with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Delighted tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Delighted and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Delighted responses and write comprehensive agent tests
Delighted MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Delighted to Pydantic AI via MCP:
add_person_to_survey
Adds a new person to the system and schedules a survey invitation to be sent via the default channel. Add a new person to Delighted to trigger a survey
get_nps_metrics_summary
Returns real-time Net Promoter Score (NPS) along with a breakdown of promoters, passives, and detractors. Retrieve overall NPS metrics, including promoter and detractor counts
get_person_feedback_history
Resolves all previous survey responses, cumulative NPS contribution, and associated person attributes. Get all feedback and metadata for a specific person
get_recent_customer_comments
List the most recent survey responses that include a text comment
get_response_details
Resolves customer details, specific survey channel, and the full text of the feedback comment. Get full details for a specific survey response
list_feedback_contacts
Returns a list of people who have interacted with Delighted, including their email addresses and survey history metadata. List people who have been sent surveys or provided feedback
list_recent_detractors
Identifies "detractors" based on an NPS score between 0 and 6. Identify customers who provided a low NPS score (0-6)
list_survey_responses
Returns response metadata including score, comment, person identifier, and timestamp. List all customer survey responses in Delighted
list_top_promoters
Identifies "promoters" based on an NPS score of 9 or 10. Identify customers who provided a high NPS score (9-10)
search_responses_by_comment
Identifies survey responses where the text matches the provided search term. Search for survey responses containing specific keywords in comments
Example Prompts for Delighted in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Delighted immediately.
"What is our current NPS score?"
"Show me the last 5 customer comments containing 'pricing'."
"Get the feedback history for 'user@example.com'."
Troubleshooting Delighted MCP Server with Pydantic AI
Common issues when connecting Delighted to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiDelighted + Pydantic AI FAQ
Common questions about integrating Delighted MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Delighted with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Delighted to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.