# Customer Rating Analytics AI Agent Connect

> Customer Rating Analytics MCP gives your AI client the ability to process and interpret star rating datasets. Instead of manually calculating averages or counting distributions, you can hand raw rating lists to your agent to get instant statistical summaries, satisfaction tier counts, and frequency distributions.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_2xQ0dlr1P6PRlWhg3DjwX0jlsuW01ef2PzDUizc9/ai-agent-connect
- **Tags:** ratings, metrics, customer-feedback, statistics, data-analysis

## Description

You shouldn't have to manually crunch numbers every time you want to understand customer sentiment. This MCP gives your AI client the math skills to handle star rating data directly. If you have a list of feedback scores, you can ask your agent to find the average, the total count, or even a rounded display rating. It goes deeper than simple averages, allowing you to see exactly how many customers fall into specific satisfaction buckets or how many people gave a specific star level. You can also use it to verify if a rating is valid before you include it in your analysis. It's a straightforward way to turn a messy list of numbers into actionable feedback metrics without leaving your chat interface.

## Tools

### calculate_rating_metrics
This tool computes the average, total count, and a rounded display rating from a provided list of star ratings.

### get_rating_distribution
This tool identifies the frequency of each star level within a dataset to show how ratings are spread out.

### get_rating_summary_by_range
This tool counts how many ratings fall within specific satisfaction tiers or numerical ranges.

### validate_rating_input
This tool checks if a single rating value is valid according to standard rating rules.

## Prompt Examples

**Prompt:** 
```
What is the statistical summary for these ratings: [5, 4, 5, 3, 5, 2, 4, 5]?
```

**Response:** 
```
The average rating is 4.125, with a total of 8 ratings. The display rating is 4.
```

**Prompt:** 
```
How many ratings are in the high satisfaction range (4 to 5 stars) for [5, 4, 3, 2, 5, 1, 4]?
```

**Response:** 
```
There are 3 ratings in the 4 to 5 star range.
```

**Prompt:** 
```
Show me the distribution of these ratings: [1, 1, 2, 3, 4, 5, 5, 5]?
```

**Response:** 
```
The distribution is: 1 star: 2, 2 stars: 1, 3 stars: 1, 4 stars: 1, 5 stars: 3.
```

## Capabilities

### Statistical Summaries
Your agent calculates averages and total counts from raw rating lists.

### Frequency Distribution
The AI identifies how many users selected each specific star level.

### Tiered Grouping
Your agent counts ratings that fall within specific satisfaction ranges.

### Data Validation
The tool checks individual rating inputs to ensure they are valid.

## Use Cases

### Sentiment Trend Analysis
Your agent compares the distribution of ratings between two different time periods.

### Satisfaction Tiering
You count how many customers fall into the 4 to 5 star range to measure high satisfaction.

### Data Cleaning
The tool validates rating inputs to catch errors in your feedback datasets.

### Quick Reporting
Get a rounded display rating and total count for a quick summary of a product's performance.

## Benefits

- Eliminates manual calculation of averages and counts.
- Groups ratings into satisfaction tiers automatically.
- Provides instant frequency counts for every star level.
- Validates rating data to ensure accuracy.

## How It Works

Connect the MCP to your client and start passing rating data to your agent.

1. Connect the MCP to your preferred client like Claude or Cursor via Vinkius.
2. Paste your list of star ratings into the chat.
3. Ask your agent to calculate metrics or distributions.
4. The agent uses the MCP tools to process the numbers and return the results.

## Frequently Asked Questions

**What can this MCP do with my star ratings?**
It calculates averages, counts total ratings, shows how many people gave each star level, and groups ratings into specific ranges.

**Which AI clients can use this MCP?**
You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

**Do I need to host this myself?**
No, Vinkius hosts and manages the MCP for you. You just connect it to your client and it is ready to use.

**Can I use it to check if a rating is valid?**
Yes, the validate_rating_input tool is specifically designed to verify if a single rating is valid.

**How does it handle satisfaction tiers?**
The get_rating_summary_by_range tool allows your agent to count how many ratings fall within specific numerical ranges you define.

**What kind of data can I analyze?**
You can analyze arrays of integer star ratings ranging from 1 to 5 using `calculate_rating_metrics` and other specialized tools.

**How do I check if a rating is valid?**
Use the `validate_rating_input` tool to confirm if a specific integer falls within the required 1 to 5 star range.

**Can I see how many people gave 5 stars?**
Yes, use `get_rating_distribution` to receive an object showing the count for every star level from 1 to 5.