Cortex Scout 是 AI Skill Hub 本期精选MCP工具之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。
Cortex Scout 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
Cortex Scout 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/cortex-works/cortex-scout
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"cortex-scout": {
"command": "npx",
"args": ["-y", "cortex-scout"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 Cortex Scout 执行以下任务... Claude: [自动调用 Cortex Scout MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"cortex_scout": {
"command": "npx",
"args": ["-y", "cortex-scout"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p> CortexScout is the Deep Research & Web Extraction module within the Cortex-Works ecosystem. </p>
<p> Designed for agent workloads that require token-efficient web retrieval, reliable anti-bot handling, and optional Human-in-the-Loop (HITL) fallback. </p>
<p> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="MIT License" /></a> <a href="https://www.rust-lang.org/"><img src="https://img.shields.io/badge/Built%20with-Rust-orange.svg" alt="Built with Rust" /></a> <a href="https://modelcontextprotocol.io/"><img src="https://img.shields.io/badge/Protocol-MCP-blue.svg" alt="MCP" /></a>
</p> </div>
---
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.
---
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.
---
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
Create cortex-scout.json in the same directory as the binary (or repository root). All fields are optional; environment variables act as fallback.
{
"deep_research": {
"enabled": true,
"llm_base_url": "http://localhost:1234/v1",
"llm_api_key": "",
"llm_model": "lfm2-2.6b",
"synthesis_enabled": true,
"synthesis_max_sources": 3,
"synthesis_max_chars_per_source": 800,
"synthesis_max_tokens": 1024
}
}
---
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.
---
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 useRUST_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 noenvcommand. 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
---
高质量的开源MCP工具,具有统一的Web提取和状态自动化功能
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,Cortex Scout 在MCP工具赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | cortex-scout |
| 原始描述 | 开源MCP工具:A unified web extraction and stateful automation engine for AI. Replaces heavy t。⭐66 · Rust |
| Topics | ai-agentsanti-bot-bypassautomated-testing |
| GitHub | https://github.com/cortex-works/cortex-scout |
| License | MIT |
| 语言 | Rust |
收录时间:2026-06-07 · 更新时间:2026-06-08 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端