context-budget

To audit token consumption, scan all loaded agents, MCP servers, and rules to calculate total token overhead. This identifies bloat and redundant components, providing prioritized recommendations for removal to free up context headroom.

Context window bloat occurs when excessive agents, rules, and MCP servers degrade AI performance and consume valuable token space. You need token-savings analysis when session performance drops or before adding new project-specific components to your environment.

Estimate context headroom by running a context budget analysis on your current session. This calculates the token overhead of existing components, determining if you have sufficient space or need to prune unused tools before adding new agents.

The best way to optimize AI context window performance is auditing token consumption across all loaded components. This identifies heavy or redundant items and provides prioritized recommendations for removal, directly improving session performance management.

Yes, context budget analysis scans all loaded components including custom MCP servers, agents, and project-specific rules. It calculates the total token overhead of these elements to identify redundant or heavy components for configuration planning.

AI performance degrades due to context window bloat, where excessive agents, rules, and MCP servers consume valuable token space. Running a token consumption audit identifies these heavy components and provides actionable optimization recommendations.