Use the perceptdot MCP server to perform AI-powered visual QA on a webpage.
Call visual_check(url) with a required url argument.
Screenshot the page with Cloudflare Browser Rendering.
Analyze page tiles with Gemini 2.5 Flash for layout bugs, with severity high/medium/low.
Run deterministic DOM checks: viewport overflow, broken images, parent overflow, low contrast.
Check desktop 1280px, tablet 768px, and mobile 375px viewports.
Use it from MCP clients such as Claude Code, Cursor, Windsurf, and Cline using an X-Percept-Key header.
Provides repository intelligence by allowing agents to retrieve pull request details, monitor issues, and check the status of CI/CD workflows.
Provides access to real-time and historical analytics data from Google Analytics 4, including sessions, pageviews, top pages, and event counts.
Integrates with Sentry to surface unresolved application errors, provide detailed stack traces, and track the health of software releases.
Enables deployment monitoring for Vercel projects, allowing agents to check deployment status, view recent commit messages, and list project states.
AI Visual QA. visual_check(url) screenshots a page with Cloudflare Browser Rendering and finds layout bugs with Gemini 2.5 Flash. Use it from any MCP client (Claude Code, Cursor, Windsurf, Cline) or as a GitHub Action.
A typical page takes about 7 seconds, long pages up to ~20 seconds. Fully headless: no desktop or GUI needed.
Get a free API key at perceptdot.com (100 credits per month, no card).
Add the remote MCP server. The key goes in the X-Percept-Key header.
Claude Code
claude mcp add --transport http perceptdot https://mcp.perceptdot.com/mcp --header "X-Percept-Key: YOUR_KEY"Cursor (~/.cursor/mcp.json)
{"mcpServers":{"perceptdot":{"url":"https://mcp.perceptdot.com/mcp","headers":{"X-Percept-Key":"YOUR_KEY"}}}}Windsurf (~/.codeium/windsurf/mcp_config.json)
{"mcpServers":{"perceptdot":{"serverUrl":"https://mcp.perceptdot.com/mcp","headers":{"X-Percept-Key":"YOUR_KEY"}}}}Then ask your AI: "Check https://your-site.com with perceptdot".
Related MCP server: token-pilot
The action lives in the eye-action/ folder of this repo.
name: Visual QA
on: [pull_request]
jobs:
visual-check:
runs-on: ubuntu-latest
steps:
- uses: perceptdot/percept/eye-action@v1.0.0
with:
url: ${{ steps.deploy.outputs.preview_url }}
api_key: ${{ secrets.PERCEPT_API_KEY }}
# fail_on_issue: true # optionalInputs: url (required), api_key (required), fail_on_issue (optional).
DOM audit (deterministic): viewport overflow, broken images, parent overflow, low contrast.
AI review: each tile of the page is reviewed by Gemini 2.5 Flash for layout problems, with severity (high / medium / low).
Desktop (1280px), tablet (768px) and mobile (375px) viewports are supported.
1 credit = 1 tile = a 1280×1600 px slice of the page. A short page is about 1 credit, a long landing page about 3–5 credits.
Plan | Price | Credits / month | Status |
Free | $0 | 100 (+100 one-time bonus after submitting feedback; resets on the 1st, UTC) | Available |
Pro | $19/month | 10,000 | Coming Soon |
Team | $49/month | 30,000 (fair use) | Coming Soon |
Payments are handled by Paddle.
Screenshots are not stored. They are sent to Google (Gemini API) and Cloudflare (Workers AI, fallback) for analysis. Analysis results (summary and issues, never screenshots) are cached for up to 10 minutes per URL and viewport. Google may use data sent to unpaid Gemini API tiers to improve its products, so do not check pages that contain personal data.
OpenAPI spec: perceptdot.com/openapi.json
Endpoints: POST /v1/eye/check, GET /v1/quota, POST /v1/free-key at https://api.perceptdot.com
curl -X POST https://api.perceptdot.com/v1/eye/check \
-H "Content-Type: application/json" \
-H "X-Percept-Key: YOUR_KEY" \
-d '{"url":"https://example.com"}'@perceptdot/ga4, @perceptdot/vercel, @perceptdot/github, @perceptdot/sentry and @perceptdot/core belong to an earlier product and are no longer maintained.
MIT. See LICENSE.
Screenshot a URL and analyze for visual bugs using AI.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to visually check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It only says 'screenshot and analyze' without mentioning whether the action is read-only, destructive, or requires permissions. No details on side effects or limits are given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence that is front-loaded. It avoids unnecessary words and effectively communicates the core function. However, it could be slightly more structured with separate purpose and usage notes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is minimally adequate. It explains what the tool does but lacks usage guidance and behavioral context. The gaps are noticeable but not critical for basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter, and the schema description 'URL to visually check' is clear. The tool description adds 'screenshot a URL' which aligns with the schema but does not provide additional meaning beyond the purpose. Hence, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: screenshot a URL and analyze for visual bugs using AI. It specifies the resource (URL) and the purpose (visual bug detection), which is direct and unambiguous. Since no sibling tools are listed, differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool or when to avoid it. There are no exclusions, prerequisites, or alternative scenarios mentioned. The description lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0visual_checkScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools.
The single tool name 'visual_check' follows a clear verb_noun pattern, which is consistent and descriptive.
Having only one tool is appropriate for a focused purpose like visual bug checking, though it is at the lower end of the typical range.
The tool fully covers its domain: taking a screenshot and analyzing it for visual bugs, with no obvious missing functionality.
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