Estimate token count for text or a message list before sending to an API. Use this to decide whether to compress, prune, or skip content.

Compress long text or conversation history into a dense summary. Use before re-injecting large context on repeated turns.

Extractive mode (default): offline, free, uses LSA sentence ranking. Abstractive mode: higher quality but requires ANTHROPIC_API_KEY env var.

Store a tool result in the persistent cache with a TTL. Prevents re-running the same expensive operation twice. Recommended: set key = make_cache_key(tool_name, args).

Retrieve a cached result. If hit, skip re-running the original tool.

Remove a stale cache entry (e.g. after file changes).

Fetch a webpage and return only its main readable content — no HTML, scripts, navigation, ads, or cookie banners. Saves 85–95% of tokens vs raw HTML.

Summarize a file or directory without reading every byte. Agents get full structural understanding in ~500 tokens instead of 50,000+.

Reduce conversation history token footprint by removing filler turns and compressing older verbose ones. Saves 60–80% on long conversations.

Shorten a verbose or redundant prompt/system prompt while preserving intent. Typical savings: 30–65%. Run once on system prompts that accumulate over iterations.

Analyze current token usage vs model context window and recommend what to trim. Use this meta-tool to know WHERE to apply compress_context, prune_conversation, or other tokensaver tools for maximum effect.