Browser-Use MCP Server

--- 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:** ```json { "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:** ```json { "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 if known, or let agent search for it 3. Agent navigates, fills fields, and submits 4. Use `task_get` to verify completion ``` run_browser_agent task: Fill out the contact form at https://example.com/contact with name "John Doe", email "john@example.com", and message "Request for demo" max_steps: 30 ``` ### Learning and Reusing Skills 1. Run `run_browser_agent` with `learn: true` to discover APIs 2. Agent records network calls and extracts patterns 3. Save skill with `save_skill_as` 4. Use `skill_list` to see learned skills 5. Reuse with `skill_name` parameter for faster execution ``` # Step 1: Learn a skill run_browser_agent task: Go to Hacker News and extract the top 10 stories with titles, URLs, and scores learn: true save_skill_as: hackernews_top_stories # Step 2: List learned skills skill_list # Step 3: Reuse the skill (faster direct execution) run_browser_agent task: Get current top stories from Hacker News skill_name: hackernews_top_stories skill_params: {"limit": 5} ``` ### Long-Running Task Management 1. Start a browser automation task (runs in background) 2. Use `task_list` to check status 3. Use `task_get` for detailed progress 4. Use `task_cancel` if needed ``` # Step 1: Start task run_browser_agent task: Research all articles on example.com blog and create a summary max_steps: 200 # Step 2: Check progress task_list status_filter: running # Step 3: Get details task_get task_id: a1b2c3d4 # Step 4: Cancel if needed task_cancel task_id: a1b2c3d4 ``` ## Advanced Features ### Skill-Based Execution When a skill is learned with API endpoints, it supports **direct execution** which bypasses the AI agent for much faster performance: - First run: Agent explores the website (60-120 seconds) - Skill learned: API patterns extracted and saved - Subsequent runs: Direct API calls (2-5 seconds) **Fallback behavior:** If direct execution fails (auth required, API changed), automatically falls back to agent-based execution. ### Progress Tracking Both `run_browser_agent` and `run_deep_research` support real-time progress tracking: - Step-by-step navigation updates - Progress percentage (current step / total steps) - Current stage (initializing, navigating, extracting, analyzing) - Task message (current action description) ### Background Task Support Long-running tasks automatically run in background when requested by the MCP client: - Tasks tracked in SQLite database - Persistent across server restarts - Query status anytime with `task_list` and `task_get` - Cancel with `task_cancel` ## Configuration The browser-use MCP server can be configured via `~/.config/mcp-server-browser-use/config.json` or environment variables. Key settings: - `browser.headless` - Run browser in headless mode (default: true) - `browser.cdp_url` - Connect to external Chrome via CDP (optional) - `agent.max_steps` - Default maximum steps (default: 100) - `research.max_searches` - Default research searches (default: 5) - `skills.enabled` - Enable skill learning and execution (default: true) - `skills.directory` - Where to store learned skills (default: ~/.config/browser-skills/) ## Troubleshooting ### Server Not Responding ``` # Check server health health_check # Check if server is running # In terminal: mcp-server-browser-use status ``` ### Task Stuck or Failing ``` # List running tasks task_list status_filter: running # Get task details task_get task_id: <task_id> # Cancel if stuck task_cancel task_id: <task_id> ``` ### Skill Execution Fails ``` # Get skill details to verify parameters skill_get skill_name: my_skill # Try without skill to re-learn run_browser_agent task: <original task> learn: true save_skill_as: my_skill_v2 ``` ## Best Practices 1. **Start with health_check** - Verify server is ready before running tasks 2. **Use descriptive task names** - Help the AI understand your intent clearly 3. **Set reasonable max_steps** - 30-50 for simple tasks, 100-200 for complex research 4. **Learn frequently-used workflows** - Save time with skill-based execution 5. **Monitor long tasks** - Use task_list and task_get to track progress 6. **Clean up failed tasks** - Use task_cancel to free resources 7. **Save research to files** - Use save_to_file to preserve research reports ## Limitations - Browser automation requires the MCP server to be running as a daemon - CDP-based browsers must be on localhost (security restriction) - Some websites may block automation (respect robots.txt and rate limits) - Skill learning requires successful task completion and API discovery - Task cancellation may take a few seconds to complete gracefully