llm-in-chrome-mcp

LLM in Chrome MCP Server

Give Claude Code a browser agent that handles web tasks autonomously.

Why This Exists

When you need to interact with the web, you have two options:

1. Low-Level Browser Tools (Playwright MCP, browser-use, etc.)

Claude: I'll click the login button
Claude: Now I'll type the username
Claude: Now I'll click submit
Claude: The page changed, let me screenshot
Claude: I see an error, let me try again
... 50 more tool calls ...

Every click, every keystroke, every navigation is a separate tool call. You're doing the browsing yourself.

2. This MCP Server (High-Level Agent)

Claude: browser_start("Log into my account and download the invoice")
Claude: browser_status → { status: "running", step: "Filling login form..." }
Claude: browser_status → { status: "complete", result: "Downloaded invoice.pdf" }

You delegate the entire task. The agent handles all browser interaction.

Tools

Tool	Description
browser_start	Start a new task. Returns session_id for tracking.
browser_message	Send follow-up instructions to a running task.
browser_status	Check progress. Works for single task or all tasks.
browser_stop	Stop a task and get partial results.
browser_screenshot	Capture current browser state.

Parallel Execution

Run multiple tasks simultaneously:

session1 = browser_start("Search for flights to Tokyo")
session2 = browser_start("Check hotel prices in Shibuya")
session3 = browser_start("Look up JR Pass costs")

# All three run in parallel
browser_status() → Shows all 3 active sessions

Multi-Turn Interaction

Send follow-up messages to guide the agent:

session = browser_start("Fill out the job application")

# Agent might need clarification
browser_status(session) → { status: "waiting", step: "What's your desired salary?" }

browser_message(session, "Put $150k")

browser_status(session) → { status: "running", step: "Completing remaining fields..." }

Session Continuation

Continue working with completed tasks - the agent retains full memory:

session = browser_start("Go to LinkedIn and find an AI Engineer job in Montreal")

# Wait for completion...
browser_status(session) → { status: "complete", answer: "Found: Applied AI Engineer at Cohere" }

# Continue with the same session - agent remembers everything
browser_message(session, "Click into that job and tell me the requirements")

browser_status(session) → { status: "complete", answer: "Requirements: 3+ years Python..." }

# Keep going
browser_message(session, "Now apply to this job using my profile")

Each session has isolated memory, so parallel tasks don't interfere with each other.

Installation

1. Install the Chrome Extension

The MCP server requires the LLM in Chrome extension to be installed and configured.

2. Install the MCP Server

cd mcp-server
npm install
npm run build

3. Configure Claude Code

Add to your Claude Code MCP config (~/.claude/claude_desktop_config.json):

{
  "mcpServers": {
    "browser": {
      "command": "node",
      "args": ["/path/to/llm-in-chrome/mcp-server/dist/index.js"]
    }
  }
}

Use Cases

Form Filling

browser_start("Apply for the senior engineer position on careers.example.com using my resume info")

Research

browser_start("Find the top 3 competitors for Acme Corp and summarize their pricing")

Data Extraction

browser_start("Go to my bank account and list all transactions from last month")

Multi-Step Workflows

browser_start("Log into Jira, find my open tickets, and summarize what needs attention this week")

How It Works

Claude Code → MCP Server → Native Host → Chrome Extension → Browser
                ↑                              ↓
                └──────── Status Updates ──────┘

• Claude Code calls browser_start with a task
• MCP server creates a session and sends to native host
• Native host relays to Chrome extension
• Extension's agent handles all browser interaction
• Status updates flow back through the chain
• Claude Code monitors via browser_status

Comparison with Other Tools

Feature	This Server	Playwright MCP	browser-use
Abstraction	Task-level	Action-level	Action-level
Tool calls per task	~3	~50+	~50+
Parallel tasks	✅	Manual	Manual
Multi-turn	✅	N/A	N/A
Setup	Extension	Playwright	Python deps

License

MIT