What is Vynix MCP Server used for?
It connects AI coding assistants to Vynix, enabling website feedback and visual bug reporting.

@UseVynix
基本情報
AI-powered website feedback, visual bug reporting, and website annotation MCP server. Connect AI coding assistants to Vynix to capture website feedback, create GitHub issues, access project data, and automate developer workflows.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"vynix": {
"command": "npx",
"args": [
"-y",
"@usevynix/mcp-server"
],
"env": {
"VYNIX_API_URL": "https://www.vynix.in",
"VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE"
}
}
}
}List the Vynix projects you own.
List a project’s annotations, optionally filtered by status, type, or priority. Start here with status "open" to see what needs fixing.
Fetch one annotation with its full captured context: page, target element (selector, XPath, styles), surrounding DOM, and runtime diagnostics (console + network).
Read the discussion thread on an annotation.
Read the latest stored AI diagnosis for an annotation (root causes, confidence, suggested fix, and likely source files). Returns null when none has been generated yet; run diagnose_annotation to create one.
Return the region screenshots attached to an annotation as viewable images, so you can see exactly what the reporter pointed at.
List the tracker (GitHub) issues opened from an annotation, with each issue’s live state. Set refresh to reconcile against GitHub (open/closed + any linked pull request).
List every tracker issue across a project, with an open/closed/agent summary.
Render a ready-to-paste coding prompt for an annotation, formatted for a target assistant. A deterministic template (no AI spend); for a deeper analysis use diagnose_annotation.
Overview of your projects: KPI counts, status breakdown, a daily time series, and recent activity.
List a project’s team members, including role and status.
Recent activity feed for a project (status changes, comments, issues, members).
Update an annotation’s status, e.g. to mark it in_progress or completed.
Add a comment to an annotation’s thread, e.g. to record what you changed. Notifies the team.
Run the AI Diagnosis Engine on an annotation: it analyses the captured page, element, DOM, and runtime errors to produce ranked root causes, a confidence score, a suggested fix, and the likely source files. Calls an AI provider (uses tokens / may cost money) and stores the result. Requires AI + diagnosis enabled by the workspace admin and a key set.
Create a GitHub issue from an annotation. Uses the project’s configured repo and assignee unless overridden. Embeds the generated AI prompt in the issue body.
Mint a read-only public review link for a project, so a stakeholder can review its annotations without an account. Reporter emails are hidden on shared views.
Vynix MCP Server connects AI coding assistants with the Vynix platform, enabling AI-powered website feedback and visual bug reporting workflows.
Install and configure the server by adding a JSON entry to your MCP client configuration, setting command to npx, args to ["-y", "@usevynix/mcp-server"], and providing VYNIX_API_TOKEN as an environment variable.
It connects AI coding assistants to Vynix, enabling website feedback and visual bug reporting.
Add the provided JSON configuration to your MCP client settings, specifying the npx command and your API token.
A secure API token, set as the environment variable `VYNIX_API_TOKEN`.
Yes, GitHub issue creation is one of its listed features.
It works with any MCP-compatible AI assistant.

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