by cortex-works · MCP Server · ★ 66
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CortexScout (cortex-scout) — Search and Web Extraction Engine for AI Agents 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. Overview 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. Tools (Capability Roster) (URL discovery) or (search+content in on
| Stars | 66 |
| Forks | 7 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.5326769865272/100 |
| Last Updated | 2026-06-07 |
| Created | 2025-08-09 |
| Platforms | browser, mcp, rust, vscode |
| Est. Tokens | ~1508k |
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cortex-scout is A unified web extraction and stateful automation engine for AI. Replaces heavy testing frameworks with token-optimized browser control, deep research, and HITL.. It is categorized as a MCP Server with 66 GitHub stars.
cortex-scout is primarily written in Rust. It covers topics such as ai-agents, anti-bot-bypass, automated-testing.
You can find installation instructions and usage details in the cortex-scout GitHub repository at github.com/cortex-works/cortex-scout. The project has 66 stars and 7 forks, indicating an active community.
cortex-scout is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cortex-scout on Agent Skills Hub include MetaSearchMCP, model-context-protocol-resources, postman-mcp-server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.