Multi-Turn Conversation Compressor

Multi-Turn Conversation Compressor MCP Connector for Claude

A+

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

2 tools Official Updated Oct 1, 2026 Official Vinkius Partner

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.

context-windowtoken-optimizationconversation-historyllm-efficiencydeterministic

2 tools expose this connector's capabilities to your AI agent.

compress_conversation_tool

evaluate_compression_quality_tool

See how to talk to your AI agent using Multi-Turn Conversation Compressor.

Compress this conversation using the summary_injection strategy with a limit of 500 tokens.

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.

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

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.

Use sliding_window to keep only the last 5 turns of this chat.

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

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.

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