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Check a Web Flow with Playwright MCP: Missing Submit Feedback

This Codex session used available Playwright MCP browser tools to open a menu, fill an email field and submit a fixture form. Two steps completed; submission produced no feedback. Inspect the screenshot, result and reproduction steps.

What Was the Task?

MCP is a convention for connecting an AI assistant to external tools. This case used Playwright MCP browser tools already available in the Codex session to check a menu, email field and form submission in user order.

Test date: 2026-10-13. The controlled web-flow fixture deliberately omits a submit handler so the check can reveal missing feedback.

Use Playwright MCP to open /data/practice/check-website.html on the current site and check only this fixture. Click “打开菜单”, fill [email protected] using the “邮箱” label, then click “提交”. Record menu state, the entered value, the page after submission and feedback text. Keep a screenshot and report any issue accurately. Do not edit files.

Actions and Observations

Outcome: the first two steps completed; the third revealed a fixture problem. The fixture has no submit handler. This is not a production-site defect, and the entire flow did not pass.

Web-flow fixture with the menu open and reader@example.test entered in the email field
Actual screenshot before submission, with the menu open and email filled. The result record separately describes the submission observation.
  1. Open the menu: completed. Its expanded state became true.
  2. Locate the email input by its label and fill the test email: completed.
  3. Submit: the form navigated or reloaded and the feedback area remained empty. Expected submission feedback was missing.

Reproduce the Check

  1. Open the fixture below. If using an AI assistant, first confirm an available Playwright MCP browser connection.
  2. Use the prompt above and inspect each actual result as you open the menu, fill the email field and submit.
  3. Compare with the record: menu opening and filling should complete, while submission gives no feedback. A clickable submit button does not establish a completed submission flow.

Environment and Limits

Recorded at 2026-10-13T01:38:22.520Z. Browser version: 155.0.8059.40. Viewport: 900 × 700. The tools did not expose the MCP package version, so none was inferred.

Existing browser tools were used without installing a new Codex MCP configuration. This does not verify installation, other browsers, mobile devices or a production site. The fixture has no real account or backend service.

Sources and ranking

This directory lists AI development plugins with public source code and a clear purpose. Browse by task, then open an introduction to learn what a project does, who it is for, and what you need to get started. It covers selected projects, not every AI plugin.

How do the three plugin types differ?

Tool connections (MCP) let AI assistants use external tools such as browsers, code repositories and databases. MCP is the protocol used for these connections.

Reusable skills give a compatible AI assistant instructions and guidance for a particular task.

Editor plugins run inside the software you use to write code, adding AI chat, completions or suggested edits.

How does “Most starred” work?

Projects are sorted by their total GitHub stars. Stars indicate interest; they are not downloads, active users or quality scores. A skill pack counts as one project. Daily star growth is not shown without historical records.

What does “Recently updated” mean?

It uses the most recent code push, which may not be a new release. The 7-day filter checks that project update time; ranking still uses all-time stars. Each introduction shows when its star count was collected. Failed updates keep the last saved data.

Introductions are based on public project documentation. These projects have not each been installed and tested here. Check the project documentation for setup, compatibility and service costs. Refreshing this list only reads saved data; it does not start collection.

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