Webvizio/mcp
by Various
Webvizio MCP Server - Automatically converts feedback and bug reports from websites and web apps into actionable, context-enriched developer tasks. Delivered straight to your AI co
MCP
Webvizio/mcp
Added 1 June 2026
Overview
Webvizio/mcp is a TypeScript MCP server that converts feedback and bug reports from websites and web apps into structured developer tasks with contextual data. It delivers these tasks directly to AI coding tools, enabling automated issue resolution.
Best for
Best for
Teams using AI coding assistants that want to automate bug report processing from web applications.
Use cases
- Convert user feedback from web apps into actionable developer tasks
- Feed context-enriched bug reports into AI coding assistants
- Streamline the feedback-to-fix pipeline for web development teams
How to use
Install
npx -y @webvizio/mcp-server Tools exposed
get_projectsget_current_projectset_projectget_tasksget_task_descriptionget_task_promptget_task_error_logsget_task_console_logsget_task_network_logsget_task_action_logsget_task_screenshotclose_task
Tested with
Cursor, Windsurf
Example client config
"webvizio": {\n "command": "npx",\n "args": [\n "-y",\n "@webvizio/mcp-server"\n ],\n "env": {\n "WEBVIZIO_API_KEY": "<your-api-key>"\n }\n} Notes
Webvizio/mcp is a TypeScript MCP server that converts feedback and bug reports from websites and web apps into structured developer tasks with contextual data. It delivers these tasks directly to AI coding tools, enabling automated issue resolution.
5 stars on GitHub. Last updated 2025-12-18. Licensed MIT.
Use cases
- Convert user feedback from web apps into actionable developer tasks
- Feed context-enriched bug reports into AI coding assistants
- Streamline the feedback-to-fix pipeline for web development teams
Pros
- Automates the conversion of unstructured feedback into structured tasks
- Provides context enrichment to help AI tools understand issues
- Integrates with MCP-compatible AI coding environments
Cons
- Requires MCP-compatible tools and setup
- Small project with limited community and documentation
- May not handle complex or ambiguous feedback accurately
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Automates the conversion of unstructured feedback into structured tasks
- Provides context enrichment to help AI tools understand issues
- Integrates with MCP-compatible AI coding environments
Cons
- Requires MCP-compatible tools and setup
- Small project with limited community and documentation
- May not handle complex or ambiguous feedback accurately
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