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AI Verify MCP

by @validpilot

MCP tools for browser validation, evidence capture, diagnostics, assertions, security checks, and structured reports.

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Install AI Verify MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "validpilot-ai-verify-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@validpilot/ai-verify-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y @validpilot/ai-verify-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 8d ago.

Overview

@validpilot/ai-verify-mcp connects AI coding workflows to browser-based validation through the Model Context Protocol. It can operate pages, capture screenshots and other evidence, run assertions and accessibility or security checks, analyze errors, and produce JSON or Markdown reports. Built-in MCP Prompts provide reusable workflows for login validation, form submission, performance and security audits, visual regression, debugging, and end-to-end flows. Reach for it when generated web code needs repeatable checks with traceable evidence rather than a pass/fail result alone.

Use cases

  • Validate an AI-generated login flow with screenshots and URL assertions
  • Capture evidence while testing a form submission
  • Run accessibility, performance, or security checks on a web page
  • Diagnose browser errors and produce a Markdown validation report

Key features

  • Browser interaction and page validation through MCP
  • Screenshot-based evidence chains with console and network records
  • Assertions for elements, text, URLs, and resulting state
  • Accessibility, performance, visual, and security checks
  • Built-in MCP Prompts for common validation workflows
  • Structured JSON and Markdown report output

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 11d ago via OSV.dev · @validpilot/ai-verify-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
npm downloads274Package downloads in the last 30 days.
Last commit1mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How AI Verify MCP works

What @validpilot/ai-verify-mcp does

@validpilot/ai-verify-mcp provides MCP tools for checking AI-generated web applications and recording the results. Its browser capabilities cover opening pages, interacting with controls, locating elements, inspecting state, capturing screenshots, and checking performance. Validation features include URL, text, and element assertions; accessibility checks using axe-core; security scans; network monitoring; and analysis of browser errors.

The server is designed around evidence-backed verification. Actions can produce screenshots, console records, network-request data, and other artifacts. These results can be assembled into structured JSON and Markdown reports, with diagnostic output that includes likely error causes, confidence information, and suggested fixes.

How it works

An MCP-compatible AI client calls the server’s tools as it works through a validation task. A typical flow opens a page, fills fields, clicks a control, checks the resulting state, and asserts that the expected URL or content appears. Evidence is collected throughout the flow so a later reviewer can inspect what happened at each stage.

The package also includes MCP Prompts for common workflows:

  • /validate-login
  • /submit-form
  • /audit-performance
  • /audit-security
  • /visual-regression
  • /debug-page
  • /e2e-flow

These prompts provide an entry point for orchestrating multiple low-level tools. Additional skill guidance is distributed with the npm package, while a Trae-specific skill extension is available as an optional enhancement.

Setup and configuration

Install the package with npm and start the MCP server with:

Terminal
npm install @validpilot/ai-verify-mcp
npx @validpilot/ai-verify-mcp start

It can also be used directly from the command line for a website validation, a named screenshot, or a quick check:

Terminal
npx @validpilot/ai-verify-mcp validate --url https://example.com
npx @validpilot/ai-verify-mcp screenshot --url https://example.com --name evidence-001
npx @validpilot/ai-verify-mcp quick-check --url https://example.com

MCP configurations shown for Cursor and Claude Code launch the package with npx. HTTP mode can be selected with MCP_MODE and a listening port with MCP_HTTP_PORT; Windsurf can use the basic npx configuration. After changing an installed version or MCP configuration, restart or reload the client.

Tools and capabilities

The toolset includes capabilities for:

  • Browser navigation, interaction, element location, state inspection, debugging, overlays, CAPTCHA handling, and sessions.
  • Screenshots, visual checks, evidence collection, chain specifications, and validation reports.
  • Assertions for elements, text, and URLs, plus smoke tests and counterfactual analysis.
  • Accessibility checks, performance analysis, network and asset inspection, and CSS variable tracking.
  • Security header checks, OWASP Top 10 checks, security scanning, and API probing.
  • Error analysis, trace correlation, MCP diagnostics, contracts, and skill validation.

Tool aliases preserve compatibility with older tool names. The server’s workflow prompts and reporting features are useful when the agent needs both execution and an auditable record of the result.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about AI Verify MCP

@validpilot/ai-verify-mcp is an MCP server that connects AI coding assistants to browser validation workflows. Its main tools operate web pages, capture screenshots and evidence, run assertions, inspect accessibility, performance, and security, analyze errors, and generate structured JSON or Markdown reports for review.

Install it with `npm install @validpilot/ai-verify-mcp`, then start it with `npx @validpilot/ai-verify-mcp start`. For an MCP client, launch the package with `npx -y @validpilot/ai-verify-mcp` in the client’s server configuration.

Yes, Cursor is documented as a supported MCP client. Add the package under Cursor’s MCP server settings or project `.cursor/mcp.json`; the example uses `npx` and can set `MCP_MODE` and `MCP_HTTP_PORT`.

Yes, it can capture screenshots during browser actions and retain console logs, network requests, and related artifacts. Its reporting tools can combine these results into structured JSON and Markdown validation reports.

Seven MCP Prompts are included: login validation, form submission, performance auditing, security auditing, visual regression, page debugging, and end-to-end flow validation. MCP-compatible clients that expose prompts can invoke these workflows from the client interface.

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