# Variable Index Dynamic Average (VIDYA) MCP for AI Agents AI Agent Connect

> Variable Index Dynamic Average (VIDYA) Calculator MCP for adaptive technical analysis. It uses the Chande Momentum Oscillator to adjust smoothing based on market volatility, allowing your AI agent to automatically scale responsiveness to price momentum and identify shifting market regimes.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_uYP5svka5R1qJSXZR1zWipqkECmdyR1ZiPCaR7uZ/ai-agent-connect
- **Tags:** trading, volatility, momentum, technical-analysis, adaptive-smoothing

## Description

Trading indicators often fail because they stay too slow when markets move fast or too jittery when markets go sideways. This MCP solves that by providing an adaptive smoothing engine that changes its behavior based on real-time volatility. Instead of using a static moving average that lags behind price action, you get a dynamic line that tightens up during high momentum and relaxes during choppy periods. You can use your AI client to generate entire datasets of smoothed values, analyze the slope of price movement, or instantly identify if the current market is in a bullish, bearish, or neutral state. By connecting this to your preferred workspace through Vinkius, you turn a standard LLM into a sophisticated quantitative analyst that understands market regimes without you having to manually calculate volatility factors or momentum oscillators.

## Tools

### calculate_vidya_series
Computes the full series of VIDYA values, slopes, and adaptive responses for a price dataset. This helps you build complete technical charts.

### detect_vidya_regime
Identifies if the market is in a Bullish, Bearish, or Neutral state. It uses the relationship between price and the adaptive line.

### get_vidya_summary
Provides a statistical snapshot of how the indicator behaved over a specific range. It's useful for quick trend reviews.

## Prompt Examples

**Prompt:** 
```
How is the trend looking for these prices: [150, 152, 155, 154, 158]?
```

**Response:** 
```
The market is currently in a **Bullish Trend**. 

* **Trend Strength:** 0.88
* **Price Position:** Above the adaptive average
* **Momentum:** Increasing

The price is showing strong upward momentum, suggesting the trend is well-established.
```

**Prompt:** 
```
Give me the smoothed values and slopes for this price list: [10, 11, 10.5, 12, 13]
```

**Response:** 
```
| Index | VIDYA Value | Slope | Volatility Response |
| :--- | :--- | :--- | :--- |
| 0 | 10.00 | 0.00 | 0.00 |
| 1 | 10.55 | 0.55 | 0.32 |
| 2 | 10.42 | -0.13 | 0.15 |
| 3 | 11.20 | 0.78 | 0.45 |
| 4 | 12.45 | 1.25 | 0.62 |
```

**Prompt:** 
```
Summarize the volatility behavior for the last 5 data points.
```

**Response:** 
```
The recent price action shows **increasing volatility**. 

* **Average VIDYA:** 11.32
* **Max Slope:** 1.25
* **Volatility Trend:** Rising

The indicator is scaling up its responsiveness as the price moves more aggressively.
```

## Capabilities

### Generate adaptive price series
Produces full datasets of smoothed values, slopes, and volatility responses for any price input.

### Identify market regimes
Determines if the current price action is bullish, bearish, or neutral based on momentum.

### Extract statistical snapshots
Provides high-level summaries of indicator behavior over specific price ranges or timeframes.

### Analyze momentum slopes
Calculates the rate of change in the adaptive average to help spot trend strength.

## Use Cases

### Detecting breakout momentum
A trader asks their agent to check if a sudden price spike is a true breakout or just noise using calculate_vidya_series.

### Filtering sideways markets
An analyst uses detect_vidya_regime to avoid entering trades when the market is in a neutral, low-momentum state.

### Backtesting adaptive strategies
A developer generates large datasets of adaptive responses to see how a strategy would have performed in high-volatility periods.

### Summarizing historical trends
A user asks for a statistical summary of a specific price window to understand recent volatility behavior.

## Benefits

- Eliminate lag by using adaptive smoothing that reacts to Chande Momentum Oscillator values.
- Identify trend shifts instantly using detect_vidya_regime to spot bullish or bearish transitions.
- Get precise trend strength data by analyzing slopes through calculate_vidya_series.
- Simplify complex trend analysis with get_vidya_summary for quick statistical snapshots.
- Automate regime detection so your agent can flag market changes without manual calculation.

## How It Works

The bottom line is you get a volatility-aware technical indicator that adapts to market speed automatically.

1. Feed your price data or specific price ranges into your AI client.
2. The MCP processes the data using the Chande Momentum Oscillator to scale the smoothing.
3. Your agent returns structured datasets, regime classifications, or statistical summaries.

## Frequently Asked Questions

**How does the Variable Index Dynamic Average (VIDYA) MCP help my trading?**
It provides an adaptive indicator that adjusts to market volatility. This means you get less lag during fast price moves and fewer false signals during sideways markets.

**Can I use this VIDYA MCP to identify market trends?**
Yes. You can use it to instantly classify whether the market is in a bullish, bearish, or neutral regime based on how price interacts with the adaptive average.

**What makes this VIDYA MCP different from a standard moving average?**
Standard averages use a fixed smoothing period. This MCP uses the Chande Momentum Oscillator to change its smoothing speed dynamically as volatility changes.

**Which AI clients can use the Variable Index Dynamic Average (VIDYA) MCP?**
You can connect this MCP to any compatible client, including Claude, Cursor, Windsurf, and VS Code.

**Can I get statistical summaries of price trends with this VIDYA MCP?**
Yes. You can request high-level statistical snapshots of the indicator's behavior over specific price ranges to understand recent momentum.