Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
npx -y [email protected]Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add codearia-sieve -- npx -y codearia-sieve@0.2.2รันในเทอร์มินัล
{ "mcpServers": { "codearia-sieve": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
An agent that needs a web page fetches the whole thing: navigation, cookie banner, footer, ad slots, a megabyte of framework markup. Then a model paid per token digs through the pile for one paragraph.
codearia-sieve does the digging before the model sees anything — and returns the page as state, not prose:
Turns web pages into decision-ready state: dates as dates, numbers computed, chunks sized for models that classify instead of write.
Dates become dates
"Published September 15, 2026" → "2026-09-15".
Read from JSON-LD, meta tags and <time> first; from a byline only when the markup is silent, and never guessed.
Numbers become facts
"$42 per billion tokens" → { value: 42, unit: "USD_per_billion" }.
Works in nine languages; the decimal comma follows the page's language. A number without a unit is not a fact. A year is never a fact.
Text becomes chunks that fit
Each chunk knows its size in tokens and characters, the blocks it was built from, and the #anchor on the page where a decision can be checked.
Everything else — menus, footers, banners, tag rows, "read more" — is removed, and with trace: true you get the list of what was removed and why.
People who build agents and have seen the bill. Every fetched page costs tens of thousands of tokens before the agent has read a word of it. Median page in the sample: 53 718 tokens in, 1 106 out.
People who run cheap decision models. Classifiers, rankers, System One models like Jev that judge instead of write. They are nearly free and very fast, and they have hard edges: they cannot count, they read dates as text, and their accuracy drops as irrelevant material fills the context. Every "page to markdown" tool prepares input for a reader. This one prepares input for a judge.
People who need answers they can check. A verdict from scraped text is unprovable unless each piece points back to its source. Here every fact names its block and every chunk carries an anchor.
Eight steps of ordinary code. No model runs unless you plug one in. The same HTML gives the same JSON, byte for byte.
robots.txt first; a refusal is reported, not bypassed. Plain HTTP, honest user agent.linkedom. No browser.<head>, bylines and attributes, so both are read before it runs.<span>20 Sept</span><span>10 min</span> never becomes 202610 min.<br><br> become paragraphs too; table rows keep their column headers.state, markdown, usage, warnings, and the trace on request.const r = await sieve({ kind: 'url', url: 'https://docs.typesafe.ai/models' });
r.state.title // "Models"
r.state.facts[0] // { value: 42, unit: "USD_per_billion", label: "price_btok_mtok",
// context: "Price (per Btok / per Mtok) | jev-1.13.0: $42 / $0.042", from: "b3" }
r.state.facts[1] // { value: 0.042, unit: "USD_per_million", … } — paired by position
r.state.chunks[0] // { id: "c1", tokens: 1210, chars: 5357, anchor: "Current models",
// headings: ["Current models", "Pricing", …], blocks: ["b1", …, "b36"], text: "…" }
r.usage // { rawTokens: 127413, stateTokens: 1211,
// visibleChars: 4939, stateChars: 5357, chunks: 1, ms: 1503 }
r.warnings // []
r.markdown // the same article, for a human or a generative model
Expected outcomes never throw. They come back as warnings, each named:
You say what you want in plain words. The agent finds the pages, calls sieve_page for each, hands the state to a decision model with a typed question, and writes up the result. Sieve prepares. The judge judges. The agent writes.
{ "mcpServers": { "sieve": { "command": "npx", "args": ["-y", "codearia-sieve"] } } }
Listed in the official MCP Registry as io.github.AntonG87/codearia-sieve; clients that read the registry can install it by name.
sieve_page — url or html
Returns typed structuredContent with an output schema: source, state, usage, warnings. In the default summary mode chunks carry sizes, anchors and their headings but no text — the agent sees the outline of what exists without paying for it. mode: "full" and mode: "markdown" when you want everything.
sieve_chunk — url, id
The text of one chunk from the last result for that URL, no refetch. Overview first, then only what is needed — the tool applies its own idea to itself.
Pairs with jev-mcp: chunks are sized to fit its fields, so state goes straight into a typed question.
The claim is that a decision model gets better input from Sieve than from raw text. So the output was handed to one. examples/jev.ts drives both MCP servers with the official client — codearia-sieve prepares six pages (API docs, a release note, two Wikipedia articles in two languages, two pricing pages), Jev judges them through jev-mcp. Same run, 21 September 2026:
The first pass of this test did its job the other way round: Jev sent three facts to review and contradicted one. All four traced to Sieve — a table row labelled by its column header instead of its row header, two rates in one header left unpaired, and a Russian bibliographic "256 с." read as seconds. Fixed, tested, rerun: 11 of 11. A judge that can tell you when your parser is wrong is the point of the whole pairing.
TYPESAFE_API_KEY=… node --experimental-strip-types examples/jev.ts
import { sieve } from 'codearia-sieve';
await sieve({ kind: 'url', url }); // fetch it
await sieve({ kind: 'html', html, url }); // already have it; url only for anchors
await sieve(input, {
budget: { maxTokens: 8000, maxChars: 30000 }, // chunk limits
trace: true, // everything discarded, and why
tokenizer: myTokenizer, // o200k by default; swap for your model's
fetcher: myFetcher, // your transport, or a file reader in tests
now: () => fixedDate, // injected clock: identical output on identical input
selector: mySelector, task: 'is this about pricing?', // relevance judge; nothing runs without one
});
parseDate, findDates and the vendor limits (JEV, JEV_MCP, DEFAULT_BUDGET) are exported too.
thin-content warning, not a fake win.empty-without-js when the container is empty. A site that ships a teaser and streams the rest cannot be told apart without a browser; you get the teaser.examples/pricing-watch.ts.usage.rawTokensEstimated: true.npm install
npm test # 71 tests, offline, a few seconds
npm run demo -- <url> # the token bill for one page
npm run bench # the 20-page benchmark set
npm run analytics # the 56-page random sample: rows, CSV, summary
Design notes — vision and architecture — are in docs/.
Turns web pages into decision-ready state: dates as dates, numbers computed, chunks sized for models that classify instead of write.
{ "mcpServers": { "codearia-sieve": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Codearia Sieve ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 Turns web pages into decision-ready state: dates as dates, numbers computed, chunks sized for models that classify instead of write.