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Context Window Optimizer

Context Window Optimizer MCP Connector for Claude

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Optimizes LLM context windows by selecting the most relevant and recent information within token limits.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Context Window Optimizer solves the context overflow problem by acting as a deterministic selection engine. It filters large pools of information to ensure AI agents receive only the most pertinent data, preventing relevance decay and exceeding token limits. Using a greedy selection strategy, it prioritizes items based on relevance scores and recency. You can use select_optimal_context to pick the best items, evaluate_selection_efficiency to measure utilization, and filter_by_relevance_threshold to prune low-quality data.

token-optimizationcontext-windowllmgreedy-selectionrelevance-scoring

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

evaluate_selection_efficiency

Measures how effectively the context was utilized relative to the total available budget

filter_by_relevance_threshold

Prunes the candidate list to remove low-quality information before performing the optimization

select_optimal_context

Determines the best set of context items to include in an LLM prompt based on relevance, recency, and token constraints

See how to talk to your AI agent using Context Window Optimizer.

Optimize this context for a 1000 token limit: [list of 50 items]

The selected contexts include the system prompt, the last user message, and the 5 highest relevance items, totaling 850 tokens.

How efficient was my last context selection?

Your utilization rate was 92% with a high relevance density of 0.85.

Filter out all context items with a relevance score below 0.5.

The filtering is complete. 12 items were removed, leaving 8 high-quality candidates.

The engine uses a deterministic greedy approach. It sorts all candidate items by relevance score (descending) and then by recency (descending), adding them to the selection until the token limit is reached.

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