Memory Context Priority Pruner

Memory Context Priority Pruner MCP Connector for Claude

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Deterministic context window management by prioritizing essential and high-relevance messages.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

The Memory Context Priority Pruner is a specialized utility designed to manage the AI's context window. It prevents context overflow by applying deterministic rules to prune conversation history. The system identifies immutable messages (system instructions and the first user message) and a protected buffer of recent messages that must remain intact. For all other middle-tier messages, it uses relevance scores to select only the most important content that fits within your specified token budget. Use analyze_context_density to audit your current usage, predict_pruning_impact to simulate strategies, and execute_context_reduction to perform the actual pruning.

context-windowtoken-optimizationdeterministic-pruningmemory-managementllm-efficiency

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

analyze_context_density

Analyzes the distribution of tokens across message tiers

execute_context_reduction

Performs the actual removal of messages to fit within budget

predict_pruning_impact

Predicts the impact of a pruning strategy

See how to talk to your AI agent using Memory Context Priority Pruner.

How many tokens are currently in my context window?

Your current message list contains 4,500 tokens, with 1,200 tokens belonging to the immutable tier and 800 tokens in your protected buffer.

If I set a target limit of 2,000 tokens, how many messages will be removed?

Applying a 2,000 token limit will save approximately 2,500 tokens by removing 15 middle-tier messages that fall below the relevance threshold.

Perform a pruning operation to fit within 3,000 tokens.

The pruning is complete. The new message list contains 2,950 tokens, and you have saved 1,550 tokens from the previous state.

The process follows strict, unchangeable rules: it always preserves system messages and the first user message, protects a recent buffer of N messages, and then selects middle messages based on their relevance score until the token budget is reached.

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