# Multi-Turn Conversation Compressor AI Agent Connect

> Deterministic engine for reducing conversation history size while preserving semantic continuity.

## Overview
- **Category:** context-management
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_cjy6h1h0uYGQWAPoJ9znJlexxrxgUhzuV2rz9OFE/ai-agent-connect
- **Tags:** context-window, token-optimization, conversation-history, llm-efficiency, deterministic

## Description

This MCP server provides a deterministic engine to manage LLM context window constraints. It allows AI agents to reduce the volume of historical conversation data using three distinct mathematical strategies: `sliding_window` for immediate context, `summary_injection` for long-term semantic retention, and `importance_based` for prioritizing critical information. The server includes tools like `compress_conversation_tool` to execute these strategies and `evaluate_compression_quality_tool` to analyze if the reduction was too aggressive or inefficient. It is designed to maintain high context continuity while maximizing token savings.

## Tools

### evaluate_compression_quality_tool


### compress_conversation_tool


## Prompt Examples

**Prompt:** 
```
Compress this conversation using the summary_injection strategy with a limit of 500 tokens.
```

**Response:** 
```
The conversation has been compressed. The historical turns have been condensed into a single summary turn, and the most recent turns have been preserved to stay within the 500 token limit.
```

**Prompt:** 
```
Check the quality of a compression where I saved 50% of tokens but the continuity score is 0.2.
```

**Response:** 
```
The compression requires review. While the token savings are efficient, the continuity score of 0.2 is below the 0.3 threshold, indicating excessive loss of conversational flow.
```

**Prompt:** 
```
Use sliding_window to keep only the last 5 turns of this chat.
```

**Response:** 
```
The conversation history has been trimmed. Only the 5 most recent turns have been retained to maintain immediate context.
```

## Frequently Asked Questions

**How does the sliding window strategy work?**
The `sliding_window` strategy uses a FIFO method to keep only the most recent turns within the specified token limit, ensuring the model has immediate context.

**Can I ensure my system prompt is never lost?**
Yes. The engine includes strict preservation rules that ensure the system prompt is always kept as the first element and is immune to all compression logic.

**How do I know if the compression was too aggressive?**
You can use the `evaluate_compression_quality_tool` to check metrics. It flags results if token savings are below 20% or if the continuity score falls below 0.3.