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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.
Route lightweight text tasks to cheaper models and save main-model tokens.
Build, debug, and manage software tasks with natural language across LLMs.
Compress and proxy MCP responses to reduce token usage for LLM tool calls.
Secure, token-efficient MySQL access for AI agents with query safeguards.
Compress prompts, tool outputs, and replies to reduce LLM token costs.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.