"author:kardashevproject" matching MCP servers:

Enables AI agents to estimate wall-clock runtime, peak memory footprint, OOM risk, and optimal batching before launching ML training or inference jobs, and to log completed runs for continuous calibration. It exposes just two ultra-compact tools so estimation fits in under 200 tokens of agent context.