Browser-use MCP Client

The Browser‑Use MCP Client is a lightweight, browser‑based interface that bridges Model Context Protocol (MCP) servers with real‑time web clients via Server‑Sent Events (SSE). It solves the common pain point of visualizing and interacting with AI agents that perform browser automation or web‑scraping tasks, without the need for custom front‑ends. By exposing a clean chat‑style UI, developers can immediately see the flow of messages, screenshots, and logs emitted by the MCP server, making debugging and iteration far faster.

At its core, the client opens a persistent SSE connection to an MCP endpoint. Every event sent by the server—whether it’s a text response, a log message, or a screenshot payload—is rendered in the conversation pane. This live streaming capability mirrors how modern AI assistants display responses, but with added context from the browser agent. The UI also provides controls for canceling in‑progress requests, clearing chat history, and toggling between light and dark themes that respect system preferences.

Key features include:

• Real‑time communication: SSE guarantees ordered, low‑latency delivery of server events.
• Screenshot preview: The client decodes base64 images sent by the MCP server and displays them inline, giving developers instant visual feedback on what the automated browser is seeing.
• Message history persistence: Chat logs are stored locally, allowing developers to revisit previous interactions without re‑running the agent.
• Request management: Users can abort long‑running browser tasks or reset the conversation with a single click, streamlining experimentation.
• Theme support: Light and dark modes adapt to the user’s system setting, ensuring a comfortable viewing experience.

In practice, this MCP client is invaluable for scenarios such as building AI‑powered web crawlers, automating form submissions, or creating conversational agents that browse the internet on demand. By visualizing each step of a browser‑controlled workflow, developers can identify bottlenecks, validate screenshot outputs, and confirm that the agent’s logic aligns with expectations. The client can be dropped into any development stack that already runs an MCP server—whether written in Python, Node.js, or another language—making it a versatile tool for rapid prototyping and production debugging alike.