ScoutUX
ScoutUX is an AI-powered tool designed to identify and highlight user experience (UX) issues on websites, applications and interfaces before they impact the end-users.
Its primary utility lies in providing rapid, objective usability feedback to aid the timely rectification of problems that could negatively affect conversions and customer retention.
The tool uses an 'automated audit' to explore the tested site, closely emulating the actions of a real human user. This includes tasks such as clicking, scrolling, and typing.
After the audit process, it offers a comprehensive report, which includes usability issue identification, and improvement suggestions, providing actionable insights based on established usability principles.
The analysis by ScoutUX is based on Nielsen's 10 Usability Heuristics for User Interface Design, considered as an industry-standard framework, trusted and adopted by many Fortune 500 companies.
ScoutUX is designed to be useful across organizations for roles like product managers, designers, developers, quality assurance professionals, founders and marketers who aim to improve user experience.
It should be noted that ScoutUX, while fast and powerful, is intended as a first-line defensive tool against usability issues, and should not be seen as a substitute for comprehensive user research.
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Pricing
Prompts & Results
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12,71499v2.1 released 2mo agoFree + from $45/moThis is the first AI marketing tool Iโve used that actually helps me get things done, not just suggest generic ideas. The mix of AI-powered strategy and real experts executing the work makes it feel like having a high-performance marketing team without the overhead
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29,036396v1.0 released 11mo agoFree + from $35/moThis team took the time to understand the industry, problem and its users and designed a perfectly engineered solution. Kudos.
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10,74712Released 1mo agoFree + from $33.95/moI would call it a "documentation generator for MCP". Would be great to see support for LM Studio too. Other than that, it looks like this tool already follows the best practices for MCP docs. For example, here is a demo for web scraping with Tavily: https://mcpshowcase.com/p/mcp-server-directory/tavily-mcp-server The docs on how to connect your MCP to different AI assistants are essential, but also a pain to maintain on your own. Also nice to see the auto-generated use cases and sample chats!
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