/browser-use

name: browser-use
description: AI-driven browser automation via Model Context Protocol

Browser Use

AI-powered browser automation for web interactions, research, and data extraction powered by the browser-use library.

When to Use

• Automate web interactions (fill forms, click buttons, navigate pages)
• Perform deep research across multiple web sources
• Extract structured data from web pages
• Learn and replay browser workflows as reusable skills
• Monitor and manage long-running browser automation tasks

Core Tools

run_browser_agent

Execute a browser automation task using AI. Supports skill-based execution, learning mode, and background task execution.

**Parameters:**

• `task` (string, required) - Natural language description of what to do in the browser
• `max_steps` (integer, optional) - Maximum number of agent steps (default: from settings)
• `skill_name` (string, optional) - Name of a learned skill to use for hints
• `skill_params` (string or dict, optional) - Parameters for the skill (JSON string or dict)
• `learn` (boolean, optional) - Enable learning mode to discover and extract APIs
• `save_skill_as` (string, optional) - Name to save learned skill (requires learn=True)

**Returns:** Result of the browser automation task. In learning mode, includes skill extraction status.

**Examples:**

# Basic usage
Search for "Claude Code plugins" on Google and summarize the top 3 results

# With max steps
Fill out the contact form at https://example.com/contact with my information
max_steps: 20

# Learning mode - discover and save a skill
Go to GitHub trending page and extract the top 5 repositories
learn: true
save_skill_as: github_trending

# Using a learned skill
task: Get trending Python repositories
skill_name: github_trending
skill_params: {"language": "python", "limit": 10}

run_deep_research

Perform multi-source research on a topic with AI-guided search and synthesis.

**Parameters:**

• `topic` (string, required) - The research topic or question to investigate
• `max_searches` (integer, optional) - Maximum number of web searches (default: from settings)
• `save_to_file` (string, optional) - Optional file path to save the research report

**Returns:** A comprehensive research report in markdown format

**Examples:**

# Basic research
What are the latest developments in AI-powered browser automation?

# With search limit
Research the security implications of CDP-based browser automation
max_searches: 10

# Save to file
Compare Playwright, Puppeteer, and Selenium for 2025
save_to_file: /path/to/research/browser-automation-comparison.md

Skill Management Tools

skill_list

List all available learned browser skills with usage statistics.

**Parameters:** None

**Returns:** JSON list of skill summaries with name, description, success rate, usage count, and last used timestamp

**Example:**

{
  "skills": [
    {
      "name": "github_trending",
      "description": "Extract trending repositories from GitHub",
      "success_rate": 95.0,
      "usage_count": 20,
      "last_used": "2025-12-20T18:00:00"
    }
  ],
  "skills_directory": "/Users/user/.config/browser-skills"
}

skill_get

Get full details of a specific skill including API endpoints, parameters, and execution hints.

**Parameters:**

• `skill_name` (string, required) - Name of the skill to retrieve

**Returns:** Full skill definition in YAML format

**Example:**

skill_name: github_trending

skill_delete

Delete a learned skill by name.

**Parameters:**

• `skill_name` (string, required) - Name of the skill to delete

**Returns:** Success or error message

**Example:**

skill_name: outdated_skill

Task Management Tools

health_check

Check if the browser automation server is running and get system statistics.

**Parameters:** None

**Returns:** JSON with server health status, uptime, memory usage, and running tasks

**Example Response:**

{
  "status": "healthy",
  "uptime_seconds": 3600.5,
  "memory_mb": 256.3,
  "running_tasks": 2,
  "tasks": [
    {
      "task_id": "a1b2c3d4",
      "tool": "run_browser_agent",
      "stage": "navigating",
      "progress": "5/100",
      "message": "Searching Google..."
    }
  ],
  "stats": {
    "total_completed": 45,
    "total_failed": 2,
    "avg_duration_sec": 32.1
  }
}

task_list

List recent browser automation and research tasks with filtering.

**Parameters:**

• `limit` (integer, optional) - Maximum number of tasks to return (default: 20)
• `status_filter` (string, optional) - Filter by status: "running", "completed", "failed", "pending"

**Returns:** JSON list of recent tasks

**Example:**

# List recent tasks
limit: 10

# List only running tasks
status_filter: running
limit: 5

# List failed tasks
status_filter: failed

task_get

Get detailed information about a specific task including input, output, and progress.

**Parameters:**

• `task_id` (string, required) - Task ID (full UUID or prefix match)

**Returns:** JSON with complete task details, timestamps, and result/error

**Example:**

task_id: a1b2c3d4

task_cancel

Cancel a running browser agent or research task.

**Parameters:**

• `task_id` (string, required) - Task ID (full UUID or prefix match)

**Returns:** JSON with success status and message

**Example:**

task_id: a1b2c3d4

Common Workflows

Web Research Workflow

1. Use `run_deep_research` with your research question 2. Review the synthesized markdown report 3. Use `run_browser_agent` for follow-up exploration of specific sources 4. Check `task_list` to monitor progress

# Step 1: Deep research
run_deep_research
topic: What are the best practices for MCP server development in 2025?
max_searches: 8

# Step 2: Follow-up investigation
run_browser_agent
task: Go to the top-ranked article and extract code examples

Form Automation Workflow

1. Use `run_browser_agent` with task describing the form 2. Include URL i