# Delighted MCP for AI Agents MCP

> Delighted connects your AI agent directly to customer feedback and experience data. It lets you monitor real-time Net Promoter Score (NPS) metrics, analyze specific survey comments for themes, and track the historical satisfaction of individual customers.

## Overview
- **Category:** customer-support
- **Price:** Free
- **Tags:** nps, customer-feedback, experience-management, sentiment-analysis, survey-responses, real-time-monitoring

## Description

Using this Delighted MCP integration, your AI client reads customer sentiment in natural language. You can get a clear picture of how satisfied your user base is without manually checking multiple dashboards. Your agent retrieves overall NPS metrics—promoters, passives, and detractors—and instantly lists the most recent comments across your platform.

Need to dig deeper? The MCP allows you to look at specific customers, pulling up their entire feedback history and associated metadata. You can also search through hundreds of comments using keywords or pull a list of people who have interacted with Delighted. This capability is huge for product managers who need to find all mentions of 'checkout flow' or 'pricing' across the entire dataset.

Connecting this MCP via Vinkius gives your AI client access to thousands of other services, keeping all your customer insights in one place. You use your agent to pull specific details about a survey response, review the full text, and even trigger new surveys for people directly through chat.

## Tools

### add_person_to_survey
Adds a new person to Delighted and automatically schedules an invitation for them to take the survey.

### get_nps_metrics_summary
Retrieves a current summary of your Net Promoter Score (NPS), including counts for promoters, passives, and detractors.

### get_person_feedback_history
Resolves the entire feedback record for one person, pulling their cumulative NPS contribution and all associated notes.

### get_recent_customer_comments
Lists the most recent survey responses that contain a written text comment from a customer.

### get_response_details
Gets full details for one specific survey response, including the channel and the exact body of the feedback comment.

### list_recent_detractors
Generates a list of customers who gave low NPS scores (0-6) so you know exactly who needs follow-up.

### list_feedback_contacts
Returns a complete list of people who have provided feedback or were sent surveys, including their email addresses.

### list_top_promoters
Identifies and lists customers who gave high NPS scores (9-10), making them candidates for testimonials.

### list_survey_responses
Lists all customer survey responses, providing metadata like the score, comment text, person ID, and date it was submitted.

### search_responses_by_comment
Searches across all comments to find specific keywords or phrases used by customers in their feedback.

## Prompt Examples

**Prompt:** 
```
What was the overall sentiment last week? Show me the breakdown.
```

**Response:** 
```
**NPS Snapshot: Last 7 Days**

| Metric | Count |
| :--- | :--- |
| **Promoters (9-10)** | 245 |
| Passives (7-8) | 62 |
| Detractors (0-6) | 18 |

*Analysis: The detractor count spiked this week. I suggest checking comments related to 'billing'.*
```

**Prompt:** 
```
Find me all the recent complaints about mobile performance.
```

**Response:** 
```
I found 5 responses mentioning poor mobile performance in the last three days. Key phrases include:

*   'Crashes on iPhone X'
*   'Slow to load outside of Wi-Fi'
*   'Unresponsive interface when I travel'

Would you like me to pull the full text for these five responses?
```

**Prompt:** 
```
What was John Doe's feedback history?
```

**Response:** 
```
**Feedback History: John Doe (user@example.com)**

*   **Most Recent:** Score 10/10, Comment: 'Seamless experience!' (Date: Today)
*   **Last Month:** Score 9/10, Comment: 'Love the new UI.'
*   **3 Months Ago:** Score 6/10, Comment: 'Kept running into issues with syncing. Needs fixing.'
```

## Capabilities

### Assess Overall Customer Sentiment
Instantly calculate and receive a breakdown of your Net Promoter Score (NPS) across promoters, passives, and detractors.

### Analyze Comment Themes
Search through all available survey responses to find comments that contain specific keywords or phrases.

### Review Individual Customer History
Fetch the complete feedback record and metadata for any single person in your database, showing their full history with you.

### Monitor New Feedback Streams
List the most recent survey responses that include a detailed text comment, keeping you updated on what customers are talking about right now.

### Identify Key Customer Segments
Generate targeted lists of high-value 'promoters' (NPS 9-10) or low-scoring 'detractors' (NPS 0-6).

## Use Cases

### Post-Launch Feature Review
A product manager needs to know what users think of the new dashboard. They ask their agent to run `search_responses_by_comment` for 'dashboard' and 'metrics'. The agent returns a list of specific quotes, instantly highlighting positive feedback alongside critical usability concerns.

### Identifying Testimonial Candidates
A marketing team wants to find advocates. They ask the AI agent to use `list_top_promoters`. The resulting list gives them names and emails of happy customers who can be immediately contacted for a quote.

### Support Escalation Context
A support rep is handling a complex ticket. They ask the agent to use `get_person_feedback_history` for the customer's email. The agent provides a summary of past scores and comments, allowing the rep to de-escalate based on historical context.

### Tracking Survey Completion
The CX lead needs a list of everyone who has given feedback so far this quarter. They ask the agent to run `list_feedback_contacts`, getting an updated roster and their last known survey status for follow-up.

## Benefits

- Find out exactly who your best customers are. Using `list_top_promoters`, you instantly pull a list of high-scoring users who are ready to give a testimonial.
- Pinpoint pain points immediately. Instead of reading hundreds of comments, use `search_responses_by_comment` to find every mention of 'login bug' or 'billing confusion'.
- Understand the risk profile. If you need to know who is unhappy, running `list_recent_detractors` gives you a clean list of people needing immediate outreach.
- Build out your customer view. By using `get_person_feedback_history`, your agent pulls all historical scores and comments for one user in a single chat reply.
- Manage the feedback loop. Use `add_person_to_survey` to trigger targeted surveys directly through conversations, getting fresh data when you need it.

## How It Works

The bottom line is that your AI agent uses this connection to perform complex, multi-step customer research without you ever leaving your chat window.

1. Connect the Delighted MCP to your AI client and authorize it using your Delighted API Key.
2. Direct your agent with a request, such as 'What is our current NPS score?' or 'Find comments about billing.'
3. The MCP executes the necessary tool calls and returns structured data—like lists of detractor emails or the full text of a survey comment—directly to your AI client.

## Frequently Asked Questions

**How does the Delighted MCP help me track NPS scores?**
It gives you a real-time summary of your Net Promoter Score, showing the exact number of promoters (happy customers), passives, and detractors. This immediate breakdown tells you where to focus your improvement efforts.

**Can Delighted MCP find specific complaints in customer comments?**
Yes. You can ask your AI agent to search all survey responses for keywords like 'checkout' or 'login bug'. It instantly pulls out every comment containing those terms, saving you hours of manual searching.

**How do I find feedback history for a single customer?**
You just ask your agent to look up the person by email. The Delighted MCP compiles all their past scores and comments into one readable summary, giving you full context on that individual's journey.

**Is this better than just using the native Delighted dashboard?**
It’s faster because your agent handles the querying. Instead of navigating multiple screens in a dashboard, you ask one question and get an immediate, conversational answer with all the necessary data points.

**What if I want to find people who were really happy?**
You can specifically request a list of 'top promoters' (NPS 9-10). This gives you an immediate, targeted list of customers you could use for case studies or testimonials.