Vynix MCP Server : AI-Powered Website Feedback & Bug Reporting

by UseVynix

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About

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.

Details

Author
UseVynix
Downloads
309
Categories
Developer Tools, Other, AI, Automation

Jump to

- Website feedback and annotations
- Visual bug reporting
- AI-ready issue context
- GitHub issue creation
- Project and annotation access
- Secure API token authentication
- Works with MCP-compatible AI assistants

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Vynix MCP Server : AI-Powered Website Feedback & Bug Reporting
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

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.

list_projects

List the Vynix projects you own.

list_annotations

List a project’s annotations, optionally filtered by status, type, or priority. Start here with status "open" to see what needs fixing.

get_annotation

Fetch one annotation with its full captured context: page, target element (selector, XPath, styles), surrounding DOM, and runtime diagnostics (console + network).

list_comments

Read the discussion thread on an annotation.

get_annotation_analysis

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.

get_annotation_screenshots

Return the region screenshots attached to an annotation as viewable images, so you can see exactly what the reporter pointed at.

list_annotation_issues

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_project_issues

List every tracker issue across a project, with an open/closed/agent summary.

generate_prompt

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.

get_metrics

Overview of your projects: KPI counts, status breakdown, a daily time series, and recent activity.

list_members

List a project’s team members, including role and status.

get_activity

Recent activity feed for a project (status changes, comments, issues, members).

update_annotation_status

Update an annotation’s status, e.g. to mark it in_progress or completed.

add_comment

Add a comment to an annotation’s thread, e.g. to record what you changed. Notifies the team.

diagnose_annotation

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_github_issue

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.

create_share_link

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "vynix mcp server : ai-powered website feedback & bug reporting": {
            "vynix": {
                "command": "npx",
                "args": [
                    "-y",
                    "@usevynix/mcp-server"
                ],
                "env": {
                    "VYNIX_API_URL": "https://www.vynix.in",
                    "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE"
                }
            }
        }
    }
}

McpServers

{
    "vynix": {
        "command": "npx",
        "args": [
            "-y",
            "@usevynix/mcp-server"
        ],
        "env": {
            "VYNIX_API_URL": "https://www.vynix.in",
            "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE"
        }
    }
}

Model Context Protocol server for Vynix. It gives coding agents direct access to visual feedback, bug reports, screenshots, diagnostics, comments, and issue workflows so agents can reason from real context instead of guessing.

- Feedback with evidence: page metadata, target element, screenshot, console/network context.
- End-to-end execution: inspect feedback, diagnose, generate coding prompts, create GitHub issues, update status, comment.
- Agent-safe hints: read-only/idempotent/open-world annotations for better approval behavior in MCP clients.
- Registry-ready metadata for modern MCP directories.

- Product screenshot placeholder: docs/assets/screenshot-dashboard.png
- Annotation workflow GIF placeholder: docs/assets/workflow-fix-annotation.gif

flowchart LR A[MCP Client\nClaude/Cursor/Copilot/VS Code] -->|stdio or streamable-http| B[Vynix MCP Server] B --> C[Vynix API] B --> D[GitHub API via Vynix backend] C --> E[Projects] C --> F[Annotations] C --> G[Screenshots and diagnostics]

- 17 production tools for read and write workflows.
- Resource catalog for server metadata, tool/prompt/skill references, and contextual summaries.
- Workflow prompts for QA, release readiness, PM briefings, and engineering planning.
- Dual transport support:stdioand Streamable HTTP.
- Auth via API token or email/password refresh flow.

{ "mcpServers": { "vynix": { "command": "npx", "args": ["-y", "@usevynix/mcp-server"], "env": { "VYNIX_API_URL": "https://www.vynix.in", "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE" } } } }

npm install -g @usevynix/mcp-server vynix-mcp

docker run --rm -i \ -e VYNIX_API_URL=https://www.vynix.in \ -e VYNIX_API_TOKEN=PASTE_YOUR_TOKEN_HERE \ ghcr.io/usevynix/vynix-mcp:latest

git clone https://github.com/UseVynix/vynix-mcp.git cd vynix-mcp npm install npm run build npm run check node dist/index.js

{ "mcpServers": { "vynix": { "command": "npx", "args": ["-y", "@usevynix/mcp-server"], "env": { "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE" } } } }

Diagnostics are written to stderr; stdout is reserved for the protocol stream.

{ "mcpServers": { "vynix": { "command": "npx", "args": ["-y", "@usevynix/mcp-server"], "env": { "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE" } } } }

Use.vscode/mcp.jsonwith top-levelservers:

{ "servers": { "vynix": { "command": "npx", "args": ["-y", "@usevynix/mcp-server"], "env": { "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE" } } } }

Use your Windsurf MCP config file with this server block:

{ "mcpServers": { "vynix": { "command": "npx", "args": ["-y", "@usevynix/mcp-server"], "env": { "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE" } } } }

For hosted mode, use the Streamable HTTP endpoint:

- Base URL:https://mcp.vynix.in/mcp
- OAuth discovery:/.well-known/oauth-authorization-server

- VYNIX_API_URL(optional, default:https://www.vynix.in)
- VYNIX_API_TOKEN(recommended)
- VYNIX_API_EMAILandVYNIX_API_PASSWORD(fallback login mode)
- VYNIX_MCP_MODE(stdioorhttp)
- VYNIX_MCP_HOST,VYNIX_MCP_PORT,VYNIX_MCP_PATH(HTTP mode)

Generate a token from:https://www.vynix.in/mcp

- Tool reference:docs/tools.md
- Prompt reference:docs/prompts.md
- Resource reference:docs/resources.md
- Skill/workflow reference:docs/skills.md
- Deployment guide:docs/deployment.md
- Agent discovery guide:docs/agent-discovery.md
- LLM index file:llms.txt
- Directory listing tracker:docs/listings-status.md

- Conversation workflows:examples/workflows
- Prompt library (100+ prompts):examples/prompts.md

- Not configurederror: setVYNIX_API_TOKENor bothVYNIX_API_EMAILandVYNIX_API_PASSWORD.
- 401errors: regenerate token and verify API URL.
- No tools listed: confirm the MCP config key (mcpServersvsservers) for your client.
- Hosted mode not reachable: verifyVYNIX_MCP_MODE=httpand check/health.

Does this send data to third-party AI providers?

Onlydiagnose_annotationcan invoke external AI providers through your Vynix workspace configuration.

No. It includes read tools and write tools. MCP annotations identify mutating/open-world calls so clients can request confirmation.

Yes. Run in stdio mode locally or HTTP mode behind your own infrastructure.

SeeCONTRIBUTING.md. For local validation run:

- Never commit API tokens.
- Prefer short-lived tokens where possible.
- SeeSECURITY.md(create one if your org requires a disclosure policy).

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Install

Add to Claude Desktop or Cursor

{
    "mcpServers": {
        "vynix mcp server : ai-powered website feedback & bug reporting": {
            "vynix": {
                "command": "npx",
                "args": [
                    "-y",
                    "@usevynix/mcp-server"
                ],
                "env": {
                    "VYNIX_API_URL": "https://www.vynix.in",
                    "VYNIX_API_TOKEN": "PASTE_YOUR_TOKEN_HERE"
                }
            }
        }
    }
}

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