Reduces token consumption by 73-87% by cleaning web and API data before it reaches the LLM context window. Supports fetching URLs, searching the web, optimizing JSON, and more.
Filters verbose terminal output from commands like npm install, pip install, docker build, and pytest, reducing context token consumption for AI agents by condensing logs, removing progress bars, and grouping repeated warnings.
An adaptive tiny-model layer that sits between an LLM and its MCP tools, compressing verbose tool outputs to reduce token usage by up to two orders of magnitude.
Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures.
Tool: compact_output. Run: npx -y logslim logslim-mcp