Browser Use
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
作者による説明(英語の原文)
Rust MCP stealth scraper: anti-bot search/scrape with CDP fallback + HITL non-robot.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
作者はレジストリにインストール情報を載せていません。下にある作者のREADMEをご覧ください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント DevsHero で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(2.3.0)がレジストリに届いたのは9 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。
同じカテゴリには:Browser Use, Chrome DevTools MCP, MCP Server Browser
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
MCP server for Chrome DevTools
MCP server for browser use access
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
CortexScout is the Deep Research & Web Extraction module within the Cortex-Works ecosystem.
Designed for agent workloads that require token-efficient web retrieval, reliable anti-bot handling, and optional Human-in-the-Loop (HITL) fallback.
CortexScout provides a single, self-hostable Rust binary that exposes search, extraction, and stateful browser automation capabilities over MCP (stdio) and an optional HTTP server. Output formats are structured and optimized for downstream LLM use.
It is built to handle the practical failure modes of web retrieval (rate limits, bot challenges, JavaScript-heavy pages) through progressive fallbacks: native retrieval → Chromium CDP rendering → Stateful E2E Testing → HITL workflows.
| Area | MCP Tools / Capabilities | ||
|---|---|---|---|
| Search | web_search (URL discovery) or web_search(include_content=true) (search+content in one call) | ||
| Fetch and Crawl | `web_fetch(mode="single" | "batch" | "crawl")` (unified fetch family) |
| Extraction | extract_fields (primary structured extraction) | ||
| Automation | scout_browser_automate / browser_automate (stateful omni-tool), scout_agent_profile_auth, scout_browser_close | ||
| Anti-bot handling | CDP rendering, proxy rotation, block-aware retries | ||
| HITL | visual_scout, `hitl_web_fetch(auth_mode="challenge" | "auth")` | |
| Memory | memory_search (LanceDB-backed research history) | ||
| Deep research | deep_research (multi-hop search + scrape + synthesis) |
Legacy names remain callable as compatibility aliases (web_search_json, web_fetch_batch, web_crawl, fetch_then_extract, human_auth_session). Agents should prefer the unified primary tools above.
While CortexScout runs as a standalone tool today, it is designed to integrate with CortexDB and CortexStudio for multi-agent scaling, shared retrieval artifacts, and centralized governance.
CortexScout includes a built-in, stateful CDP automation engine designed specifically for AI Agents, completely replacing heavy frameworks like Playwright or Cypress for E2E testing workflows.
steps. The runtime now covers Playwright-style action families in one call: navigation, hover/click/type/wait, locator-driven actions, assertions, tabs, screenshots/PDF, file upload, form fill, dialog policy, coordinate mouse actions, route mocking, console/network capture, and cookie/storage CRUD.--headless=new) using a dedicated isolated profile (~/.cortex-scout/agent_profile). It maintains cookies, localStorage, and session state across tool calls without causing SingletonLock collisions with your active desktop browser.mock_api, route_list, unroute) with response header overrides/stripping, trace flows (trace_start, trace_stop, trace_export), capture console/network logs, checkpoint browser state, and run both CSS and locator-based assertions plus Playwright-style verification helpers.| Capability Area | Cortex Scout Actions |
|---|---|
| Navigation and input | navigate, navigate_back, click, hover, type, press_key, scroll, wait_for, wait_for_selector, wait_for_locator |
| Locator and verification | click_locator, type_locator, assert, assert_locator, generate_locator, verify_element_visible, verify_text_visible, verify_list_visible, verify_value |
| Tabs and media | tabs, resize, screenshot, snapshot, pdf_save, file_upload, fill_form, handle_dialog |
| Network and mocks | network_tap, network_dump, network_state_set, mock_api, route_list, unroute |
| Browser state | storage_clear, storage_state_export, storage_state_import, storage_checkpoint, storage_rollback, cookie_*, localstorage_*, sessionstorage_* |
| Low-level pointer control | mouse_click_xy, mouse_down, mouse_move_xy, mouse_drag_xy, mouse_up, mouse_wheel |
The main tradeoff versus raw Playwright MCP is packaging, not capability shape: Cortex Scout keeps the browser surface inside one stateful omni-tool so agents spend fewer turns and fewer tokens coordinating multi-step flows.
This repository includes captured evidence artifacts that validate extraction and HITL flows against representative protected targets.
| Target | Protection | Evidence | Notes |
|---|---|---|---|
| Cloudflare + Auth | JSON · Snippet | Auth-gated listings extraction | |
| Ticketmaster | Cloudflare Turnstile | JSON · Snippet | Challenge-handled extraction |
| Airbnb | DataDome | JSON · Snippet | Large result sets under bot controls |
| Upwork | reCAPTCHA | JSON · Snippet | Protected listings retrieval |
| Amazon | AWS Shield | JSON · Snippet | Search result extraction |
| nowsecure.nl | Cloudflare | JSON | Manual return path validated |
See proof/README.md for methodology and raw outputs.
Download the latest release assets from GitHub Releases and run one of:
cortex-scout-mcp — MCP stdio server (recommended for VS Code / Cursor / Claude Desktop)cortex-scout — optional HTTP server (default port 5000; override via --port, PORT, or CORTEX_SCOUT_PORT)Health check (HTTP server):
./cortex-scout --port 5000
curl http://localhost:5000/health
Install protoc first. lance-encoding uses Protocol Buffers during the release build, so protoc must be on your PATH.
brew install protobufsudo apt-get install -y protobuf-compilersudo dnf install -y protobuf-compilerBasic build (search, scrape, deep research, memory):
git clone https://github.com/cortex-works/cortex-scout.git
cd cortex-scout cargo build --release --manifest-path mcp-server/Cargo.toml --bin cortex-scout-mcp
This works from the repository root because the manifest path is explicit.
Full build (includes hitl_web_fetch / visible-browser HITL):
cargo build --release --manifest-path mcp-server/Cargo.toml --all-features --bin cortex-scout-mcp
If you also want the optional HTTP server binary, build it explicitly with cargo build --release --bin cortex-scout.
Local MCP smoke test:
python3 publish/ci/smoke_mcp.py
This runs a newline-delimited JSON-RPC stdio session against the local cortex-scout-mcp binary and exercises the main public tools with safe example inputs.
Add a server entry to your MCP config.
VS Code (mcp.json — global, or settings.json under mcp.servers):
The hard timeout guard vars below are required in MCP configs. They are the safety rail that prevents a bad page, stalled browser launch, or stuck scrape stage from holding the whole MCP session open indefinitely.
// mcp.json (global): top-level key is "servers"
// settings.json (workspace): use "mcp.servers" instead { "servers": { "cortex-scout": { "type": "stdio", "command": "env", "args": [ "RUST_LOG=warn", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS=90", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_SCRAPE_URL=90", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_SEARCH_STRUCTURED=120", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_VISUAL_SCOUT=45", "CORTEX_SCOUT_BROWSER_LAUNCH_TIMEOUT_SECS=12", "CORTEX_SCOUT_BROWSER_TAB_PROBE_TIMEOUT_SECS=4", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS=20", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_CDP_INITIAL_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_CDP_RETRY_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_FORCED_CDP_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_NATIVE_CDP_FALLBACK=25", "SEARCH_ENGINES=google,bing,duckduckgo,brave", "LANCEDB_URI=/YOUR_PATH/cortex-scout/lancedb", "HTTP_TIMEOUT_SECS=30", "MAX_CONTENT_CHARS=10000", "/YOUR_PATH/cortex-scout/mcp-server/target/release/cortex-scout-mcp" ] } } }
Default behavior is direct/no-proxy. Add IP_LIST_PATH and PROXY_SOURCE_PATH only if you want proxy tools available. If you want proxy_control available without routing normal traffic through proxies, point IP_LIST_PATH at an empty ip.txt file and let agents populate it on demand.
Important: Always use
RUST_LOG=warn, notinfo. Atinfolevel, the server emits hundreds of log lines per request to stderr, which can confuse MCP clients that monitor stderr.
Windows: Windows has no
envcommand. Use thecommand+envobject format instead — see docs/IDE_SETUP.md.
With deep research (LLM synthesis via OpenRouter / any OpenAI-compatible API):
{
"servers": { "cortex-scout": { "type": "stdio", "command": "env", "args": [ "RUST_LOG=warn", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS=90", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_SCRAPE_URL=90", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_SEARCH_STRUCTURED=120", "CORTEX_SCOUT_TOOL_TIMEOUT_SECS_VISUAL_SCOUT=45", "CORTEX_SCOUT_BROWSER_LAUNCH_TIMEOUT_SECS=12", "CORTEX_SCOUT_BROWSER_TAB_PROBE_TIMEOUT_SECS=4", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS=20", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_CDP_INITIAL_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_CDP_RETRY_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_FORCED_CDP_ATTEMPT=25", "CORTEX_SCOUT_SCRAPE_STAGE_TIMEOUT_SECS_NATIVE_CDP_FALLBACK=25", "SEARCH_ENGINES=google,bing,duckduckgo,brave", "LANCEDB_URI=/YOUR_PATH/cortex-scout/lancedb", "HTTP_TIMEOUT_SECS=30", "MAX_CONTENT_CHARS=10000", "OPENAI_BASE_URL=https://openrouter.ai/api/v1", "OPENAI_API_KEY=sk-or-v1-...", "DEEP_RESEARCH_LLM_MODEL=moonshotai/kimi-k2.5", "DEEP_RESEARCH_ENABLED=1", "DEEP_RESEARCH_SYNTHESIS=1", "DEEP_RESEARCH_SYNTHESIS_MAX_TOKENS=4096", "/YOUR_PATH/cortex-scout/mcp-server/target/release/cortex-scout-mcp" ] } } }
Multi-IDE guide: docs/IDE_SETUP.md
Create cortex-scout.json in the same directory as the binary (or repository ro
冒頭だけを表示しています。続きはリポジトリで読めます