Delighted MCP. Track NPS and analyze customer comments in real time.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Delighted MCP Server connects your AI client directly to your customer feedback data. Use it to monitor real-time NPS scores, pull specific survey responses, and track customer feedback history.
It lets your agent automatically schedule surveys or pinpoint specific groups like promoters and detractors.
What your AI agents can do
Add person to survey
Adds a new contact to Delighted and triggers a survey invitation to be sent via the default chat channel.
Get nps metrics summary
Returns the overall Net Promoter Score (NPS) and breaks down the count into promoters, passives, and detractors.
Get person feedback history
Retrieves all past survey responses, cumulative NPS, and metadata for a single person.
Retrieves the overall Net Promoter Score (NPS) and provides a breakdown of the customer base into promoters, passives, and detractors.
Gets the full history of responses and metadata for any specific customer identified in your database.
Lists the most recent survey responses, focusing specifically on those that include detailed text comments.
Searches all survey responses to find comments that contain specific keywords, helping you pinpoint common themes.
Filters the customer base to list only the top promoters (NPS 9-10) or the most critical detractors (NPS 0-6).
Adds new contacts to Delighted and automatically sends them a survey invitation via the default channel.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
Delighted MCP Server: 10 Tools for CX & Feedback Analysis
These tools let your AI agent run complex customer experience analysis—from calculating NPS to searching specific comments—without leaving your chat window.
019d7583add person to survey
Adds a new contact to Delighted and triggers a survey invitation to be sent via the default chat channel.
019d7583get nps metrics summary
Returns the overall Net Promoter Score (NPS) and breaks down the count into promoters, passives, and detractors.
019d7583get person feedback history
Retrieves all past survey responses, cumulative NPS, and metadata for a single person.
019d7583get recent customer comments
Lists the most recent survey responses that include a text comment.
019d7583get response details
Retrieves the full text and metadata for a specific survey response.
019d7583list feedback contacts
Lists people who have provided feedback or been sent surveys, including their emails and history metadata.
019d7583list recent detractors
Finds customers who scored low on the NPS scale (0-6).
019d7583list survey responses
Lists metadata for all customer surveys, including the score, comment, person ID, and time stamp.
019d7583list top promoters
Finds customers who scored high on the NPS scale (9-10).
019d7583search responses by comment
Searches all survey responses for text comments matching a specific keyword.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Delighted, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Delighted MCP Server connects your AI client straight to your customer feedback data. You can use it to track real-time Net Promoter Scores (NPS), pull specific survey responses, and look at a customer's entire feedback history. Your agent can also automatically send surveys or pinpoint specific groups, like your biggest fans or the folks who are having a rough time. get_nps_metrics_summary gives you the overall NPS score and breaks down the count into promoters, passives, and detractors. get_person_feedback_history lets you pull all past survey responses, the cumulative NPS, and metadata for any single person.
You can see the most recent survey responses that include a text comment using get_recent_customer_comments. If you need to dig into themes, search_responses_by_comment searches all survey responses for text comments matching a specific keyword. You can find your best customers by running list_top_promoters, which finds people who scored high on the NPS scale (9-10).
Conversely, list_recent_detractors finds the customers who scored low (0-6). To check all the responses, list_survey_responses lists metadata for every customer survey, including the score, comment, person ID, and time stamp. You can also list everyone who's given feedback or who you've sent surveys to using list_feedback_contacts, getting their emails and history metadata.
When you need to get a person's details, get_response_details retrieves the full text and metadata for a specific survey response. You can manage your contacts by using add_person_to_survey, which adds a new contact to Delighted and sends a survey invitation via the default chat channel. list_survey_responses lists metadata for all customer surveys, including the score, comment, person ID, and time stamp.
How Delighted MCP Works
- 1 Connect the Delighted integration to your AI client.
- 2 Authorize the connection using your Delighted API Key (find this in your account settings).
- 3 Your agent can then access real-time customer satisfaction data through natural conversation.
The bottom line is, your AI agent reads the Delighted API so you don't have to switch apps to see customer sentiment.
Who Is Delighted MCP For?
CX Leads who need to identify immediate pain points. Product Managers who need to link specific feedback themes to the roadmap. Support Teams who need to see a customer's full history before talking to them.
Checks a customer's feedback history using get_person_feedback_history before calling them about a retention issue.
Searches for specific themes across all feedback using search_responses_by_comment to guide the next product sprint.
Runs get_nps_metrics_summary to track overall customer health and identify if the negative trends are concentrated among detractors using list_recent_detractors.
What Changes When You Connect
- Track overall customer health immediately. Use
get_nps_metrics_summaryto get the current NPS score and a precise breakdown of promoters, passives, and detractors. - Deep dive into specific customer issues.
get_person_feedback_historypulls every response and piece of metadata for one user, so you don't have to piece together their journey. - Spot emerging product themes. Run
search_responses_by_commentto find all comments mentioning 'checkout' or 'login', instantly grouping feedback by keyword. - Manage your survey flow. Use
add_person_to_surveyto add a contact and trigger a survey invite, making feedback collection part of your regular chat flow. - Prioritize follow-up. Use
list_recent_detractorsandlist_top_promotersto get curated lists of the most critical or most enthusiastic customers. - Audit your data easily.
list_survey_responsesgives a comprehensive list of every response, including the score, comment, and time stamp, for quick auditing.
Real-World Use Cases
Investigating a sudden drop in NPS score
A CX Analyst notices the NPS score dipped last week. They ask their agent to run get_nps_metrics_summary to confirm the current count. They then use list_recent_detractors to pull the list of critical users, and finally run get_person_feedback_history on the top three accounts to find the systemic root cause.
Gathering feedback for a new feature
A Product Manager needs to know how users feel about pricing. They ask their agent to run search_responses_by_comment using the keyword 'price'. This instantly pulls all relevant comments, allowing the PM to map direct feedback to the roadmap.
Re-engaging an inactive, high-value user
A CSM wants to check in with a key account. First, they run list_feedback_contacts to verify the user's last interaction date. Then, they use add_person_to_survey to send a targeted survey, bringing the user back into the feedback loop.
Reviewing a complex support ticket
A Support Agent has a difficult ticket. They ask their agent to run get_person_feedback_history to see if the user has reported similar issues before, or if their previous NPS scores are consistently low, informing the agent's tone and resolution.
The Tradeoffs
Treating feedback as a simple list
Manually pulling the last 100 responses from a dashboard, trying to read for patterns, and mixing in data points from other systems. This is slow, prone to missing context, and requires heavy spreadsheet work.
→
Use list_survey_responses for a raw data dump, then use search_responses_by_comment to filter that list by a specific theme (e.g., 'bug') to cut through the noise.
Focusing only on the score
Seeing a low NPS score and assuming the problem is universal. This ignores the fact that the low score might only come from one specific segment of users.
→
Run get_nps_metrics_summary for the overall view, but immediately follow up with list_recent_detractors to identify the specific customers and their comments that caused the dip.
Ignoring customer context
Responding to a support ticket without knowing the customer's history. This leads to repetitive questions and a poor user experience.
→
Always run get_person_feedback_history first. This provides the full context and shows if they've reported the issue before, saving time and improving service.
When It Fits, When It Doesn't
Use this server if your primary bottleneck is synthesizing customer sentiment from multiple data sources. You need to move beyond just seeing data and start acting on it. If you need to calculate the overall health (NPS), start with get_nps_metrics_summary. If you need to solve a specific product problem, run search_responses_by_comment to find the text. Don't use this if you just need a simple list of names; use list_feedback_contacts. Don't use it if you only need to track billing data; this tool focuses purely on experience metrics and survey responses.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Delighted. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually tracking customer sentiment is a nightmare.
Today, gathering customer feedback means jumping between the Delighted dashboard, your CRM, and your internal Jira board. You have to copy scores, cross-reference IDs, and manually write up summaries. It's slow, and you always miss the context that connects a low score to a specific comment.
With this MCP server, you just ask your agent: 'What's the overall customer sentiment?' The agent runs the necessary tools and gives you a summarized, actionable answer—the NPS score, the top themes, and the list of people who need follow-up. Period.
Delighted MCP Server: Get the customer feedback history.
Without the `get_person_feedback_history` tool, checking a user's history means navigating multiple tabs: looking at their last survey, then checking their profile attributes, then finding the score they gave last month. It’s a multi-click, multi-system headache.
Now, your agent retrieves the full history in one command. You get the total count of responses, the scores, and the raw text—all context you need to solve the issue in a single chat exchange.
Common Questions About Delighted MCP
How do I find the current NPS score using the `get_nps_metrics_summary` tool? +
The get_nps_metrics_summary tool immediately returns your current NPS score. It also provides a clean breakdown, showing the exact counts for promoters, passives, and detractors based on recent responses.
Can I use `search_responses_by_comment` to find issues related to pricing? +
Yes. search_responses_by_comment searches all responses for a specific keyword. This lets you filter out general noise and focus only on feedback that mentions 'pricing' or 'cost'.
What is the difference between `list_survey_responses` and `get_recent_customer_comments`? +
The list_survey_responses tool gives metadata for every single survey response. get_recent_customer_comments is narrower; it only lists the most recent responses that actually include a text comment.
How do I find all the people who have given feedback? +
Run list_feedback_contacts. This tool lists all people who have interacted with Delighted, providing their email addresses and a record of their overall survey history.
What is the best way to find the most valuable customers? +
Use list_top_promoters. This tool identifies customers who gave high NPS scores (9-10), giving you a ready-made list of people you can ask for referrals or case studies.
How do I check a single person's full feedback history using `get_person_feedback_history`? +
The get_person_feedback_history tool retrieves all responses, cumulative NPS, and associated metadata for a specific person. You provide the person's identifier, and the tool returns their entire feedback record, letting you track their satisfaction over time.
Can I find the list of people who have been invited to a survey using `list_feedback_contacts`? +
Yes, list_feedback_contacts returns a list of people who have interacted with Delighted. This list includes their email addresses and survey history metadata, helping you manage your user base.
What happens if I need to search for comments containing specific keywords using `search_responses_by_comment`? +
The search_responses_by_comment tool identifies survey responses where the text matches your provided search term. This lets you quickly pull all comments containing specific keywords, like 'login error' or 'shipping delay'.
How do I get a Delighted API Key? +
Log in to your Delighted account, navigate to Settings > API, and you can retrieve your unique API Key from that section.
Can I see real-time NPS updates? +
Yes, you can use the get_nps_metrics_summary tool to see the latest calculated score and breakdown of responses.
Does the integration support multiple survey types? +
Yes, Delighted supports NPS, CSAT, CES, and other survey types. The agent will retrieve responses regardless of the survey methodology used in your account.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
Helpjuice
Automate knowledge base management via Helpjuice — manage articles, categories, and analytics directly from any AI agent.
CleverTap
Manage customer engagement and analytics via CleverTap — track campaigns, monitor user segments, and audit event data directly from any AI agent.
GoZen Testimonials
Collect and showcase video testimonials from happy customers with branded recording pages and embeddable display widgets.
You might also like
Hugging Face
Explore AI models, datasets and Spaces via Hugging Face — search models, inspect files, review discussions and track collections from any AI agent.
OKX
Trade crypto and manage your OKX account via AI — check balances, track positions, and execute orders directly from your agent.
PrecisionConvert Unit Engine
Universal unit conversion intelligence — transform physical values via AI.