token budget optimization

MCP Search Tool saves 10s of thousands of tokens by deferring tool definition loading—concrete token optimization technique

The entire strategy is built on the insight that delegating routine work to cheaper models reduces overall token costs

Explicitly addresses token cost through model selection and task routing—this is applied token optimization

Core technique is allocating token budget across model tiers based on task phase criticality—planning/correction uses premium model, execution uses standard model.

The insight directly addresses reducing token consumption for meta-context while preserving human readability.

Token cost spikes on misrouted requests suggest context window management is critical to both quality and cost in multi-agent systems

The 3000-character limit is an explicit token budget constraint; this is a practical instantiation of managing context window costs.

Per-teammate model selection (Opus for debugger, Haiku for quality) is explicit token/cost optimization based on task requirements

Mentions 'limited context window (maximum number of tokens)' as the core constraint that context engineering must solve for.

ICE depends on strategic token placement and composition within context windows. This validates that not all tokens are equal—position, sequencing, and curation matter.

Author's solution assumes per-account token budgets should be shareable across sessions, which contradicts per-session token tracking design