Delighted MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Delighted through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"delighted": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Delighted, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Delighted through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Delighted MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Delighted via MCP
Why Use LangChain with the Delighted MCP Server
LangChain provides unique advantages when paired with Delighted through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Delighted MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Delighted queries for multi-turn workflows
Delighted + LangChain Use Cases
Practical scenarios where LangChain combined with the Delighted MCP Server delivers measurable value.
RAG with live data: combine Delighted tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Delighted, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Delighted tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Delighted tool call, measure latency, and optimize your agent's performance
Delighted MCP Tools for LangChain (10)
These 10 tools become available when you connect Delighted to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Delighted to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersDelighted + LangChain FAQ
Common questions about integrating Delighted MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.