io.github.dong7812由 GitHub 帳號 dong7812 發布v0.5.2更新於 Oct 27, 2026
Strips layout noise via DOM AST; with a query, BM25 filters to relevant sections. No model or API.
擷取已知網址並回傳經 DOM 精簡的 Markdown，在進入助理上下文前把網頁 token 減少約九成以上。
Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。
把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。
閱讀 https://sourceweft.com/mcp/io.github.dong7812%2Fdompruner-mcp/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。把它新增到你客戶端的 mcpServers 設定中。
{
"mcpServers": {
"dompruner": {
"type": "http",
"url": "https://dompruner-mcp.vercel.app/api/mcp"
}
}
}한국어 | English
[DOM Tree Pruning for DomPruner]
DOM AST middleware for LLM web pipelines — strips layout noise (nav, scripts, sidebars) and passes original text directly. Add a query to filter to relevant sections with BM25.
When an LLM uses a web fetch tool, the raw page HTML is converted to text and passed into the model's context — often tens of thousands of tokens of navigation, sidebars, and boilerplate the model doesn't need. DomPruner strips that at the DOM level and passes only the original content directly to the model.
93.5% fewer context tokens than WebFetch on average. 45% faster end-to-end. → Full benchmark
No installation, no API key:
Add to .mcp.json in your project root, or ~/.claude/.mcp.json for global. Run /mcp to verify.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
For clients that support HTTP transport — no Node.js install required, always runs the latest version:
langchain-mcp-adapters wraps any MCP stdio server as LangChain tools automatically:
DomPruner's tool description already tells clients to prefer dompruner_fetch over WebFetch. If your client still falls back, add this to its instruction file:
Measured using Anthropic's web_fetch_20260209 server tool under identical conditions (same model, system prompt, tool definitions). → Full methodology
headingBoost=2.0 was selected by running a parameter sweep (1.0–3.0) on the tuning set. The validation set uses five sites not present in the tuning set (react.dev, typescriptlang.org, expressjs.com, doc.rust-lang.org, nextjs.org) to confirm the result is not overfit. The 2 %p gap is within the known heading-bias limitation.
→ Full benchmark · Architecture
Crawl4AI is a crawler framework — browser automation, JS rendering, click/scroll interaction, JSON extraction, deep crawl. DomPruner is LLM context middleware — a single-purpose layer that reduces token cost when an agent already knows the URL.
They solve different problems. But both can be used to read a web page inside an MCP agent, so the comparison is real.
The most defensible comparison: five sites that were not used to tune DomPruner's parameters (react.dev, typescriptlang.org, expressjs.com, doc.rust-lang.org, nextjs.org). Both tools use the same query.
On sites DomPruner was not tuned on: 1.9× fewer tokens with the same accuracy (96%).
On DomPruner's own tuning pages, vs Crawl4AI PruningContentFilter: 13.2× smaller, 8 pp more accurate (90% → 98%). Crawl4AI BM25ContentFilter with the same query returns near-empty output on 5 of 10 pages — a structural issue with <dl>/<dt>/<dd> definition-list pages where a threshold sweep (0.0–0.10) shows a hard cliff at 0.01 with no intermediate optimum.
→ Full comparison with all tables and threshold sweep data
DomPrunerLoader, DomPrunerSitemapLoader, DomPrunerFetchTool for LangChain. pip install dompruner.MIT
來源：README.md，提交 d8c1bb9
Fetch & Prune Web Page
Fetches a URL and returns DOM-pruned Markdown with 90%+ token reduction. Optimized for Developer Documentation, API Specs, and Technical Blogs (Next.js/Nuxt/SSR). Always prefer this over raw WebFetch when the URL is known.
urlqueryAnalyze Token Reduction
Returns a token-reduction analysis report for a URL. Shows render type (SSR/CSR/SSG), original vs refined token counts, reduction ratio, and top Semantic Anchors.
url必填已滿足適用檢查項的 100%
該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。
> [DomPruner] docs.python.org> | Raw HTML | 44,316 tokens |> | DomPruner | 1,328 tokens |> | Reduction | 97.0% |> Fetch: 194ms · Parse: 11.2msnpx -y dompruner-mcp{ "mcpServers": { "dompruner": { "type": "stdio", "command": "npx", "args": ["-y", "dompruner-mcp"] } }}{ "mcpServers": { "dompruner": { "command": "npx", "args": ["-y", "dompruner-mcp"] } }}{ "mcpServers": { "dompruner": { "type": "stdio", "command": "npx", "args": ["-y", "dompruner-mcp"] } }}{ "mcpServers": { "dompruner": { "url": "https://dompruner-mcp.vercel.app/api/mcp" } }}from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({ "dompruner": { "command": "npx", "args": ["-y", "dompruner-mcp"], "transport": "stdio", }})tools = await client.get_tools()When retrieving a URL, always use dompruner_fetch instead of WebFetch.- URL known → dompruner_fetch(url, query?)- URL unknown → search for the URL first, then dompruner_fetch(url)