Lean Reader

Turn any URL into token-minimized clean text for LLMs, with a token-savings receipt on every call. MCP server + library.

LLMs don't need your nav bar, your cookie banner, your <script> tags, or 200 KB of inlined SVG — but raw page HTML makes them pay for all of it. Lean Reader strips a page down to the article and tells you exactly how many tokens (and dollars) you just saved.

231,276 → 15,735 tokens (93% saved · 14.7× vs raw HTML · ~$0.54 on gpt-4o) · cleaned by lean reader

Use as an MCP server

Add to your client's MCP config (Claude Desktop/Code, Cursor, …):

{
  "mcpServers": {
    "lean-reader": { "command": "npx", "args": ["-y", "lean-reader"] }
  }
}

Then the lean_read(url, format?) tool returns clean text plus the receipt.

Use as a library

import { leanRead } from 'lean-reader/lib/core.js';

const r = await leanRead('https://example.com/article', { format: 'markdown' });
console.log(r.content);   // token-minimized text
console.log(r.receipt);   // { beforeTokens, afterTokens, savedPct, ratio, estCostSavedUsd, ... }

How much does it save?

Measured, not marketed — the open benchmark ships the corpus, the tokenizer, and every raw output, and flags the cases where Lean Reader loses:

~29% fewer tokens than Mozilla Readability (the standard extractor) at the median, while keeping ~99% of the body text. Be honest about where that edge comes from: it's the minimize post-pass (link/image/footnote/whitespace strip), not smarter extraction — run both through minimize and they're roughly par. Lean actually runs Readability as one of its two extractors (see Honest limits), so it doesn't lose to it.
Versus raw page HTML the multiple is much larger (median ~8.7×, down to ~3.1× on already-clean blog prose, 100×+ on script-heavy docs) — but that's HTML nobody feeds an LLM, so read it as "don't dump raw pages," not as a competitive claim.
Versus Jina Reader (measured, anonymous tier): ~1.6× fewer tokens on a like-for-like body, ~4.3× if you count the nav and reference dumps Jina also returns. Firecrawl is not yet measured (needs an API key).

The receipt uses the o200k_base tokenizer (GPT-4o/4.1 class); the model and tokenizer are always shown, and counts are vs the raw page HTML so you can check the math.

Honest limits

Static HTML only (v1). Pages whose body is client-rendered (some SPAs, GitHub repo landing pages) return little — Lean Reader flags partial instead of emitting empty text. Jina/Firecrawl render JS and will beat us there.
Two extractors, body-max selection. Defuddle and Mozilla Readability each silently drop the body on different pages (Defuddle on some large Wikipedia articles, Readability on some docs/SPAs). Lean runs both and keeps whichever recovers more body, so neither's blind spot becomes a silent content drop. A ROUGE-L ground-truth pass on a 14-page hand-labeled sample is done: reference-body recall 0.99, equal to Readability on the same ground truth, so the word-count gap is noise removal, not body loss (see the bench repo).
Token counts are o200k_base; Claude/Gemini tokenize differently.

Open-core

The extraction + token-minimization core (lib/) and the MCP server (src/) are MIT. Hosted service, sharing UI, and metering are separate.

lean-reader