Web Eval Agent MCP Server

Overview

The operative.sh web‑eval‑agent MCP server equips AI assistants with the ability to autonomously launch a real browser, interact with a web application, and produce detailed quality‑assurance reports. By exposing two primary tools— for automated UX evaluation and for persistent browser sessions—it resolves the long‑standing challenge of testing front‑end behavior directly from an IDE chat. Developers no longer need to switch context to separate test suites; the assistant can invoke the agent, execute a natural‑language task, and return a comprehensive report that includes screenshots, console logs, network traces, and UX observations.

What the Server Does

When a developer asks an AI assistant to “evaluate my app at http://localhost:3000,” the server launches a non‑headless browser (or headless if requested), navigates to the specified URL, and performs actions described in a human‑readable task. It captures every network request, filters irrelevant traffic, and aggregates console errors. The resulting UX report is returned as structured text that can be displayed directly in the editor’s chat pane, enabling instant feedback on bugs, performance issues, or design inconsistencies. The tool lets developers sign in once and reuse authenticated sessions across multiple evaluations, eliminating repetitive login steps.

Key Features

• BrowserUse Integration – Utilizes operative’s backend to double the navigation speed, making interactive testing snappy.
• Rich Contextual Output – Returns screenshots, console logs, and network traffic within the chat context for quick triage.
• Autonomous Debugging Loop – The Cursor agent can call the web‑eval‑agent to verify that newly written code behaves end‑to‑end, creating a self‑checking development cycle.
• Persistent Session Management – Cookies and local storage are preserved between runs, enabling tests that require authentication or stateful interactions.
• Natural‑Language Task Definition – Developers describe what to test in plain English, and the agent interprets those instructions into browser actions.

Use Cases

• Rapid Front‑End QA – Quickly validate signup flows, checkout processes, or dynamic dashboards without writing separate test scripts.
• Regression Testing – Integrate the agent into a CI pipeline to automatically re‑evaluate critical paths whenever code changes are committed.
• Design Review – Capture visual regressions and UI inconsistencies that might be missed by automated screenshot comparisons alone.
• Security & Performance Audits – Inspect network traffic for unexpected requests or payloads, and log console errors that could indicate hidden bugs.

Integration with AI Workflows

Developers embed the web‑eval‑agent into their IDE chat, allowing an AI assistant to trigger browser sessions with a single command. The assistant can chain multiple tools: first establishing an authenticated session via , then invoking to run a specific test scenario. The resulting report can be fed back into the assistant’s reasoning loop, enabling iterative refinement of code or design. Because the server exposes its capabilities through the MCP interface, any AI client that supports MCP can consume these tools without custom adapters.

Unique Advantages

The web‑eval‑agent stands out by combining real‑browser interaction with AI‑driven task interpretation in a lightweight, cloud‑agnostic service. Its ability to persist browser state and capture granular network/console data provides a depth of insight that traditional headless test frameworks often miss. For developers who rely on AI assistants for rapid iteration, this MCP server transforms the browser from a static testing target into an active participant in the development loop.