# ATR-Calculator MCP for AI Agents AI Agent Connect

> ATR-Calculator MCP for measuring market volatility. It calculates the Average True Range as a percentage of price and classifies volatility levels as low, medium, or high. You can also check price data integrity and pull historical percentile boundaries to ground your technical analysis in real market context.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_OKkGwiAWn6aKP42VAcraydGwCL77425DZ1piduYN/ai-agent-connect
- **Tags:** atr, volatility, trading, finance, indicators

## Description

Measuring market volatility shouldn't feel like a math homework assignment. Instead of manually calculating ranges or squinting at messy spreadsheets, you can now hand your price data directly to your AI client and get instant, deterministic volatility metrics. This MCP handles the heavy lifting of technical analysis, turning raw price arrays into actionable insights. You can determine exactly how much a security is swinging relative to its price or check if your data is even clean enough to run an analysis. It even lets you pull historical percentile boundaries so your agent knows if current volatility is actually unusual or just business as usual. By connecting this to your workflow through the Vinkius catalog, you turn your agent into a sharp technical analyst that understands market movement without you having to write a single line of math code.

## Tools

### validate_price_data
Checks that your price arrays are mathematically consistent and ready for analysis. It catches errors like low prices being higher than high prices.

### calculate_atr
Computes the Average True Range as a percentage of price. It also assigns a volatility classification of low, medium, or high.

### get_volatility_thresholds
Fetches historical percentile boundaries. This helps you understand if current volatility is truly extreme.

## Prompt Examples

**Prompt:** 
```
What is the volatility for these prices? High [150, 155, 152], Low [145, 148, 147], Close [148, 152, 150].
```

**Response:** 
```
The volatility for this dataset is **Medium**. 

**Details:**
* **ATR:** 3.5%
* **Classification:** Medium
```

**Prompt:** 
```
Are these price arrays valid for analysis? High [10, 12], Low [11, 9], Close [10.5, 10].
```

**Response:** 
```
No, the data is **invalid**. 

**Error Found:** The low price in the second period (9) is lower than the high price (12), but the sequence logic indicates a mismatch in the provided array structure.
```

**Prompt:** 
```
How does the current volatility compare to historical levels using a 14-period window?
```

**Response:** 
```
Based on the historical percentile boundaries:

| Volatility Level | Threshold |
| :--- | :--- |
| **Low** | Below 1.2% |
| **Medium** | 1.2% to 2.5% |
| **High** | Above 2.5% |

Currently, the market is sitting in the **Medium** range.
```

## Capabilities

### Classify market volatility
Get an immediate low, medium, or high rating for current price swings.

### Calculate price-relative ATR
Determine the Average True Range as a percentage to compare different assets.

### Audit price data integrity
Ensure your price arrays are mathematically sound before running any analysis.

### Retrieve volatility boundaries
Pull historical percentile thresholds to contextualize current market movement.

## Use Cases

### Adjusting stop-losses for high volatility
A trader notices a sudden spike in movement and asks their agent to calculate the ATR to set a wider stop-loss that won't get hunted.

### Cleaning messy data feeds
An analyst has a corrupted CSV of prices and uses the agent to validate the data before attempting to run a strategy.

### Identifying breakout opportunities
A user checks if current volatility is hitting historical highs to confirm a true market breakout.

### Comparing asset volatility
A portfolio manager compares the relative volatility of Bitcoin against Gold using percentage-based ATR.

## Benefits

- Eliminate manual math errors by letting your agent handle the ATR calculations.
- Get instant volatility classifications using calculate_atr to judge market regimes.
- Avoid broken models by using validate_price_data to scrub your price arrays first.
- Contextualize price swings by pulling historical boundaries with get_volatility_thresholds.
- Compare different assets easily by viewing ATR as a percentage of price rather than raw points.

## How It Works

The bottom line is you get instant, mathematically accurate volatility metrics from raw price data.

1. Provide your price data arrays to your AI client.
2. The agent runs the math to check for data errors and calculate the range.
3. You receive a clear volatility classification and percentage-based ATR.

## Frequently Asked Questions

**How can I use ATR-Calculator to analyze market movement?**
You can provide price data to your agent, and it will use the ATR-Calculator MCP to determine the Average True Range as a percentage and classify the volatility level.

**Can this MCP help me check for errors in my trading data?**
Yes, it includes a tool specifically designed to validate price arrays, ensuring your high, low, and close prices are mathematically consistent before you perform analysis.

**How does ATR-Calculator define high volatility?**
Volatility is classified as high when the calculated ATR exceeds the historical percentile boundaries retrieved for that specific dataset.

**Is it possible to compare volatility between two different stocks?**
Yes, because the tool calculates ATR as a percentage of the price, you can compare the relative volatility of a cheap stock against an expensive one accurately.

**Can I use ATR-Calculator with Claude or Cursor?**
Yes, as long as you are using an MCP-compatible client like Claude, Cursor, or Windsurf, you can connect this MCP and start running volatility calculations immediately.

**What is the difference between the smoothing methods?**
The server supports Wilder's smoothing (the traditional method), Simple Moving Average (SMA), and Exponential Moving Average (EMA).

**How is volatility classified?**
Volatility is classified as low, medium, or high based on where the current ATR percentage sits within the historical distribution of the provided data.

**Can I validate my price data before calculating ATR?**
Yes, you can use the `validate_price_data` tool to check if your high, low, and close price arrays are consistent and valid.