$ loading_
Cut AI API costs dramatically with token measurement, compression, caching, and pruning.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "TokenSaver MCP" yet — see the docs or source repo.
Use TokenSaver MCP to analyze our current AI request traffic, measure token usage, and suggest compression, caching, and pruning strategies that reduce API costs without changing existing prompts.
Provides token usage analysis, cost-saving optimization strategies, and estimated savings.
Using TokenSaver MCP, design a low-cost AI calling strategy for my app, focusing on request caching, context compression, and pruning unnecessary content, with clear implementation steps.
Outputs an actionable integration plan explaining each optimization method and implementation steps.
Use TokenSaver MCP to compare token usage before and after optimization, showing measurement results, cache-hit benefits, and the overall API cost reduction.
Generates a before-and-after report with key metrics and overall cost reduction findings.
Compress and proxy MCP responses to reduce token usage for LLM tool calls.
Lets AI access design tokens and component contracts via MCP consistently.
Track LLM token usage, estimate costs, and monitor API requests via proxy.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.
Reduce token usage in Claude Code with automatic context optimization.
Provide AI agents with developer utilities like code review, JSON formatting, and password generation.