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Track LLM token usage, estimate costs, and monitor API requests via proxy.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP TokenSage" yet — see the docs or source repo.
Use TokenSage to analyze this set of LLM API call logs, count tokens per request, total usage, and model-grouped costs, then output a summary table.
A table showing per-call and total token usage and costs, summarized by model.
Explain how to configure TokenSage in proxy mode to intercept and monitor Cursor LLM API requests and log token usage and costs.
Step-by-step proxy setup instructions, integration details, and what request and cost data can be monitored.
Use TokenSage to compare token usage, response overhead, and estimated costs across different models for the same task set, and identify the most cost-efficient option.
A comparison of usage and costs across models, with recommendations for the most cost-saving choice.
Cut AI API costs dramatically with token measurement, compression, caching, and pruning.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.
Secure, token-efficient MySQL access for AI agents with query safeguards.
Count tokens, estimate costs, optimize prompts, and compare LLM pricing.
Compare AI model pricing, simulate costs, and get plan recommendations.
Monitor Claude usage live, predict limits, and control costly AI coding actions.