Toolgz – cut LLM tool-definition tokens ~80% without hurting accuracy
toolgz Your agent spends 30–50k tokens of context on tool definitions before the user types a word. toolgz gets ~80% of it back. 420-run cross-provider sweep · 4 frontier models · zero runtime dependencies · generated before/after npm install toolgz The problem You connect a few MCP servers. Every tool is a JSON Schema with a sentence of prose per parameter.
- ▪toolgz Your agent spends 30–50k tokens of context on tool definitions before the user types a word. toolgz gets ~80% of it back.
- ▪420-run cross-provider sweep · 4 frontier models · zero runtime dependencies · generated before/after npm install toolgz The problem You connect a few MCP servers.
- ▪Every tool is a JSON Schema with a sentence of prose per parameter.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,312 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/dperussina/toolgz |
| Publication time | Sat, 25 Jul 2026 21:05:43 +0000 |
| Retrieval time | 2026-07-25T21:18:45.334Z |
| Last seen | 2026-07-25T21:18:45.334Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | y4YzXoNyNvRz |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
toolgz Your agent spends 30–50k tokens of context on tool definitions before the user types a word. toolgz gets ~80% of it back. 420-run cross-provider sweep · 4 frontier models · zero runtime dependencies · generated before/after npm install toolgz The problem You connect a few MCP servers. Each ships 20–50 tools. Every tool is a JSON Schema with a sentence of prose per parameter. That block renders at the front of every single request. A realistic tool definition is ~420 tokens, and roughly 400 of them are prose the model doesn't need in order to pick correctly. Fifty tools is 20k tokens. A hundred is 40k. Prompt caching makes those tokens cheap. It does not make them take up less room. Reclaiming the room is what this does.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.