About Webvizio MCP
Webvizio MCP Server connects MCP clients to Webvizio projects and development tasks, giving coding agents task details and supporting evidence such as logs and screenshots.
Key features
- 01List and select Webvizio projects
- 02Retrieve open tasks and descriptions
- 03Fetch task prompts, logs, and screenshots
- 04Mark tasks as done
Use cases
- 01Give a coding agent context for a reported bug
- 02Review task reproduction steps and diagnostic logs
- 03Close a Webvizio task after completion
MCP support
The TypeScript MCP server provides structured access to the Webvizio API. MCP clients can retrieve project and task information, inspect supporting data, and update task status.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
12Saved tool list · updated 3w ago
This tool takes no parameters.
Details
From the project READMEAbout Webvizio
Webvizio streamlines communication between end-users, non-technical teams, and developers.
Our easy-to-use browser extension lets you capture visual feedback and bug reports directly on a web page. It then automatically gathers all relevant data and converts it into actionable, context-enriched development tasks for your AI coding agents.
From non-technical product feedback to bug-free code in seconds, not days. Learn more about Webvizio for developers here.
Webvizio MCP Server
A TypeScript-based Model Context Protocol (MCP) server that provides secure and structured access to the Webvizio API. This server enables MCP clients to seamlessly interact with Webvizio projects and development tasks through a standardized interface.
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 coding tools, the Webvizio MCP Server ensures your AI agent has all the data it needs to solve tasks with speed and accuracy.
📋 Available Tools
| Tool | Method | Description |
|---|---|---|
get_projects | GET | Fetch all available Webvizio projects |
get_current_project | GET | Get details of the currently selected project |
set_project | POST | Set the current Webvizio project to work with the project tasks |
get_tasks | GET | Get the list of open tasks assigned to a user |
get_task_description | GET | Get the task description |
get_task_prompt | GET | Get an AI prompt to execute the task |
get_task_error_logs | GET | Get the task error logs |
get_task_console_logs | GET | Get the task console logs |
get_task_network_logs | GET | Get the task network logs |
get_task_action_logs | GET | Get the task action logs (Repro steps) |
get_task_screenshot | GET | Get the task screenshot |
close_task | POST | Set task in Webvizio to Done status |
🛠 Installation
- Make sure you have Node.js 18 (or a higher version) installed on your device.
- Copy the MCP server configuration code to the
mcpServerssection of your AI Client configuration file."webvizio": { "command": "npx", "args": [ "-y", "@webvizio/mcp-server" ], "env": { "WEBVIZIO_API_KEY": "<your-api-key>" } } - Generate and copy the API key for the MCP server on the AI Settings page https://app.webvizio.com/profile/ai
- Insert the API key into the
WEBVIZIO_API_KEYenvironment variable. - Save the changes.
- Done. Your MCP server is configured and ready to work.
🚀 Usage
- Open your IDE's chat (e.g., agent mode in Cursor or Cascade in Windsurf).
- Paste a link to a Webvizio task. Alternatively, you can ask the AI agent to display all your tasks.
- Instruct the AI agent to execute the selected task.
- The AI agent can request the use of tools to gather more information on the task (e.g., screenshots, console logs, etc.). Approve these requests to allow the agent to proceed.
- Check the agent's work to ensure the task has been completed correctly.
- When the task is complete, ask the AI Agent to close the task.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.