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.

Related MCP server: superFetch MCP Server

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.

MIT © 2026