Delighted MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Delighted as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Delighted. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Delighted?"
)
print(response)
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.
LlamaIndex agents combine Delighted tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Delighted MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Delighted MCP Server
LlamaIndex provides unique advantages when paired with Delighted through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Delighted tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Delighted tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Delighted, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Delighted tools were called, what data was returned, and how it influenced the final answer
Delighted + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Delighted MCP Server delivers measurable value.
Hybrid search: combine Delighted real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Delighted to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Delighted for fresh data
Analytical workflows: chain Delighted queries with LlamaIndex's data connectors to build multi-source analytical reports
Delighted MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Delighted to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Delighted to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDelighted + LlamaIndex FAQ
Common questions about integrating Delighted MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.