Turbo-Browser-MCP 将浏览器工具合并为 10 个批量接口

原标题：Show HN: Turbo-Browser-MCP – Batch forms, DOM deltas and submit guards for MCP

62AI 研判分

Turbo-Browser-MCP 是基于 Playwright 的 MCP 服务器，把 25-33 个细粒度浏览器原语合并为 10 个批量工具，通过增量 DOM 快照、单轮表单预填与磁盘直传文件显著降低 token 消耗与多轮延迟。基准显示密集 UI 快照从 18,654 token 降至 1,135 token，点击后增量快照仅 56 token，并默认启用提交前人工确认保护。

关联知识实体

工具Playwright 术语增量 DOM 快照 MCP模型上下文协议工具Turbo-Browser-MCP

报道正文

本文目录2 个章节

turbo-browser-mcp

turbo-browser-mcp is a Playwright-based Model Context Protocol (MCP) server built to cut LLM token waste and multi-turn latency in browser automation. It consolidates 25-33 fine-grained browser primitives into 10 batched tools featuring delta DOM snapshots, single-turn React/Shadow DOM/iframe form prefill, direct disk file uploads (no Base64 in context), parallel tab sessions, network blocking presets, and a default human-in-the-loop Submit guard.

At-a-Glance Performance & Token Scorecard

All figures below come from benchmarks/benchmark.mjs (npm run benchmark) running headless Chromium locally with exact OpenAI o200k_base tokenization (tiktoken 0.14.0):

Capability	Typical Baseline	`turbo-browser-mcp`	Speed / Turn Gain	Token Savings (`o200k_base`)
Dense UI Snapshot (140-btn SaaS console)	`18,654` tok HTML / `3,568` tok full ARIA	`1,135` tok compact (`28 ms`)	`1` call	`93.9%` vs HTML (`68.2%` vs ARIA)
Post-Click Delta Snapshot (`onlyChanged: true`)	`3,582` tok full ARIA rescan	`56` tok changed lines (`18 ms`)	`1` call	`98.4%` vs ARIA (`99.7%` vs HTML)
10-Field Complex Form + Snapshot (`smart_fill_form`)	`11` turns (`574 ms`, `12,197` dir / `72,614` cum tok)	`1` turn (`82 ms`, `811` tok)	`7x` faster, `11 -> 1` turns	`93.4%` direct / `98.9%` cumulative
48 KiB PDF Upload (`49,152` bytes)	`44,728` tok inline Base64 tool arg	`85` tok disk path (`68 ms`)	Zero Base64 generation	`99.8%` saved
6-Step Interactive UI Flow (`batch_actions`)	`6` turns (`411 ms`, `955` dir / `3,162` cum tok)	`1` turn (`357 ms`, `406` tok)	`1.15x` faster, `6 -> 1` turns	`57.5%` direct / `87.2%` cumulative
Open 6 Tabs Concurrently (`open_many`)	`6` turns sequential (`998 ms`)	`1` turn batched (`420 ms`)	`2.38x` faster, `6x` fewer turns	`5` fewer prompt replays
Heavy Media Load (`preset: "turbo"`, `304 KiB` assets)	`252 ms`, `9` media requests (`304 KiB`)	`165 ms`, `0` media requests (`0 KiB`)	`1.53x` faster	`100%` media bandwidth cut
Article Markdown Scraping (`extract_clean_text`)	`3,595` tok raw HTML (`10,844` chars)	`156` tok clean Markdown (`641` chars)	Clean reader output	`95.7%` saved
Diagnostics Triage (`page_diagnostics` errors-only)	`3,460` tok unfiltered (`32` req / `26` console)	`601` tok default errors (`2` req / `6` console)	Zero noise scrolling	`82.6%` saved
Static MCP Tool Schema (every turn overhead)	`3,917` tok Playwright / `6,737` tok DevTools	`3,010` tok stripped (`3,314` full w/ annotations)	`10` tools vs `25` / `33`	`23.2%` vs Playwright / `55.3%` vs DevTools

Note on latency vs LLM inference turns: Eliminating 10 round-trips on a 10-field form saves both 7x in browser execution (574 ms -> 82 ms) and 10 separate LLM inference round-trips (2-5s per turn in agent loops) plus ~71,800 replayed history tokens.

Why Browser Agents Stall: Side-by-Side Workflow

Standard MCP servers charge an LLM round-trip for every snapshot, field fill, <select> choice, upload, and verification check. Each turn re-sends the growing conversation history back into the model.

（注：更多技术实现细节与完整 API 文档请查阅原项目 README）