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Measure CPU energy and LLM token usage to guide efficient refactoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "green-mcp" yet — see the docs or source repo.
Use green-mcp to measure CPU energy and LLM token usage for this Python service before and after refactoring. Run each version 10 times and report averages, variance, and optimization suggestions.
A comparison report with energy use, token consumption, deltas, and recommendations on whether further refactoring is worthwhile.
Analyze this script with multiple model calls using green-mcp. Identify the functions or steps with the highest CPU energy and token usage, then rank them by cost.
A hotspot list showing the most energy-intensive and token-heavy steps, with a prioritized optimization order.
Use green-mcp to measure response time, CPU energy, and token accounting accuracy for the same request batch before and after adding the non-blocking token proxy, then summarize the benefits.
An evaluation of the proxy integration covering performance impact, measurement quality, and whether it should be enabled in production.
Analyze code structure and Git history while drastically reducing AI token usage.
Cut AI API costs dramatically with token measurement, compression, caching, and pruning.
Build, debug, and manage software tasks with natural language across LLMs.
Run dev checks and get compact error summaries for faster debugging.
Give AI coding agents filesystem, Git, database, and compute tools via MCP.
Give AI full visibility into your local development environment for faster troubleshooting.