Browserbear MCP. Automate web scraping and visual tests from your agent.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Browserbear MCP Server automates web interactions directly from your AI client. It lets you scrape structured data, take high-quality screenshots, and run complex, multi-step browser tasks without leaving your chat window.
Use the server to list tasks, trigger runs with URL overrides, and manage project history instantly.
What your AI agents can do
Create task
Saves a new, reusable browser automation task definition.
Delete run
Removes a specific record of a completed task run from the history.
Get account usage
Retrieves current usage statistics for your Browserbear account.
Retrieves detailed metadata for every automation task you've saved in your account.
Runs a specific task, allowing you to override inputs like the starting URL or form data, and monitors the run's progress.
Captures a high-quality image of a specified URL, letting you define exact dimensions and wait times.
Retrieves usage statistics and manages the list, inspection, and deletion of past task runs.
Lists all projects in your account and allows you to manage task scope.
Ask AI about this MCP
Supported MCP Clients
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019d7564create task
Saves a new, reusable browser automation task definition.
019d7564delete run
Removes a specific record of a completed task run from the history.
019d7564get account usage
Retrieves current usage statistics for your Browserbear account.
019d7564get run
Checks the status and results of a specific, past task run.
019d7564get task
Retrieves detailed setup information for a single, saved automation task.
019d7564list projects
Lists all projects you have set up in your account.
019d7564list runs
Retrieves a list of all completed task run records.
019d7564list tasks
Lists all browser automation tasks you have saved.
019d7564run task
Initiates a live run of a specific, saved browser automation task.
019d7564take screenshot
Captures an image of a specified URL with custom dimensions and wait times.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Browserbear, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Yo, this Browserbear MCP Server lets your AI client run web tasks, scrape data, and take screenshots right from your chat. It treats your agent like a remote browser. You can manage everything—from listing your saved tasks to triggering full runs and cleaning up history—all without leaving your window. You'll find that you can list all saved browser automation tasks using list_tasks and get detailed setup information for a single task with get_task.
You can save a new, reusable automation definition with create_task.
Need to run something? You can initiate a live task run with run_task, letting you override inputs like the starting URL or form data. You'll monitor the run's progress and check its final results using get_run. You can also manage your projects by calling list_projects to see what you've set up, and you can delete old run records using delete_run.
When you need a quick visual check, take_screenshot captures a high-quality image of any URL; you can even set custom dimensions and wait times for it. You can keep track of your usage and history by calling get_account_usage to see your current stats, or list_runs to get a full list of past attempts.
How Browserbear MCP Works
- 1 Subscribe to the Browserbear MCP Server and enter your API key.
- 2 Tell your AI client the goal (e.g., 'Run the competitor scraping task on the new product page').
- 3 The agent calls the relevant tool (e.g.,
run_task), and you get the status and results back in the chat.
The bottom line is, your AI client executes web actions against your browserbear account, and you get the results right where you're talking to it.
Who Is Browserbear MCP For?
The QA Engineer who has to manually run visual regression tests across 20 different pages before every release. The Growth Marketer who needs to gather competitor pricing data daily. The developer who wants to integrate complex browser flows into their code editor instead of writing a separate script.
Triggers visual regression tests and inspects results for multiple URLs without opening a dashboard.
Automates lead gathering or competitor monitoring straight from their workflow tools.
Integrates multi-step browser interactions (like login flows or form submissions) into their natural language coding environment.
What Changes When You Connect
- Need to check if a checkout page looks right after a UI update? Use
take_screenshotto grab a high-fidelity image of the URL. You control the dimensions and wait times, so the capture isn't cut short. - Running tests shouldn't require switching tabs. With
list_tasksandrun_task, you tell your agent to trigger a full automation flow and monitor the results—all within the chat window. - Tracking competitor data is easier than ever. Your agent can run a task, scrape the structured data, and dump the results right into your workflow, bypassing manual copy/pasting.
- Project management is streamlined. Use
list_projectsto see all your work areas andget_account_usageto keep tabs on your total consumption. - Debugging complex flows is faster. Instead of guessing, use
get_taskto inspect the full details of a specific task before you run it, ensuring all inputs are correct. - Cleaning up history is simple. If a run failed or is no longer needed, use
delete_runto remove the record using the run ID.
Real-World Use Cases
Validating a new feature's layout
A QA engineer needs to confirm that the login page still looks correct after a UI refresh. They ask their agent to run take_screenshot on the login URL, specifying a 1920x1080 resolution. The agent captures the image, and the engineer reviews it immediately, avoiding manual dashboard work.
Gathering competitor pricing data
A marketing manager needs to track the pricing of a rival's flagship product. They use the agent to run a predefined task via run_task, which scrapes the data. The agent returns the structured JSON data, allowing the manager to aggregate it directly into a spreadsheet.
Debugging a complex user flow
A developer needs to test a multi-step form submission. They use the agent to call create_task to build the flow, then use run_task with an override URL to test it against a staging environment. The agent monitors the run and confirms the sequence works.
Auditing project usage
A product owner wants to know how much API budget the team has spent on web scraping this month. They simply ask the agent to call get_account_usage, and the agent returns the raw usage metrics instantly.
The Tradeoffs
Treating the server as a static API endpoint
Writing a script that calls list_tasks to get IDs, then looping through them to call run_task one by one, and finally calling get_run for each ID. This is slow and requires massive boilerplate code.
→
Instead, let your agent manage the sequence. Ask the agent to 'Run all my tasks and report the status.' The agent handles the list_tasks -> run_task -> get_run sequence internally, keeping your conversation clean.
Manually managing run IDs
Getting a run ID from a dashboard, copying it, pasting it into the prompt, and then calling get_run. This is three steps and requires context switching.
→
Just ask your agent, 'What was the result of the last run for the News Scraper task?' The agent handles the list_runs and get_run calls automatically, keeping the focus on the data, not the IDs.
Assuming immediate results
Calling run_task and expecting the final scraped data immediately. Complex tasks take time to execute and process.
→
Always ask the agent to monitor the task. Use the agent to call run_task and then follow up with, 'Check the status of that run.' This tells the agent to use get_run until the job completes.
When It Fits, When It Doesn't
Use this server if your goal is to perform visual checks, data scraping, or multi-step automation directly from a natural language chat interface. You need to test a website's appearance or extract structured data without opening a browser or writing a dedicated script.
Don't use this if you only need to fetch simple, static data (like a user list) or if your workflow requires interacting with a system outside of a web browser. For pure data fetching, look for a simple REST API tool. If you need to manage a large number of resources, use list_runs or list_tasks first to map out the scope before running any operations.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Browserbear. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually checking web data is a huge time sink.
Every time you need to confirm a price point, check a form, or see if a page looks right, you're forced to open a tab, navigate to the URL, and manually copy/paste the data or take a screenshot. If you're checking a competitor's site, you're clicking through dashboards, dealing with CAPTCHAs, and running the same sequence of clicks over and over.
With the Browserbear MCP Server, you just tell your agent to 'Check the pricing page and scrape the table.' The agent executes the task, gathers the data, and presents it to you. You get the structured output immediately, no clicks, no dashboard navigation.
Browserbear MCP Server: Run web tasks and capture visuals
Instead of building custom Python scripts just to take a screenshot, you call `take_screenshot`. You specify the URL, the resolution, and the wait time right in your prompt. The agent handles the rest, providing a clean, usable image.
The difference is that you don't write code to run a test. You just describe the test. The agent translates that description into a full, reliable automation run, giving you reliable results every time.
Common Questions About Browserbear MCP
How do I list all my browser automation tasks using the create_task tool? +
You use the list_tasks tool to retrieve all saved task definitions. This shows you exactly what you've set up before you try to run anything. You can then use run_task with the task ID.
What is the difference between run_task and take_screenshot? +
run_task executes a full, predefined automation workflow (like filling forms or scraping). take_screenshot just grabs a picture of a single, static URL at a specific moment.
Can I override the URL when I run_task? +
Yes. When you call run_task, you can pass dynamic overrides, such as changing the starting URL or providing specific form data. This lets you test the same task against different environments.
How do I check if a task run was successful? +
You first use list_runs to find the run ID, then use get_run with that ID. The result tells you the status and provides the final data or screenshot link.
How do I manage my project settings using list_projects? +
The list_projects tool shows all your existing work areas. You can use this to organize your tasks and understand which project a given task belongs to.
How do I check my account usage with the get_account_usage tool? +
The get_account_usage tool provides statistics on your account's usage. You can see how many tasks you've run and what your remaining capacity is.
What happens when I use the delete_run tool? +
The delete_run tool removes a specific task run record. This helps you clean up old history and keeps your run logs tidy.
Do I need to manually list tasks before I can run them using the list_tasks tool? +
No, you don't have to list them first. You can use the list_tasks tool to see all available tasks, but you can run a task directly by knowing its ID.
Can I trigger a browser task with a different starting URL? +
Yes! Use the run_task tool and provide the Task ID. In the overrides field, you can pass a JSON object like {"url": "https://newsite.com"} to change the starting point dynamically.
How do I see the screenshot captured by a task? +
Simply ask the agent to get_run and provide the Run ID. Once the status is 'finished', the response will include a screenshot_url that you can click to view the image.
Does the integration allow taking a quick screenshot without a saved task? +
Yes. Use the take_screenshot action and provide the URL. It will create a simplified run that captures the page and returns the result URL once processed.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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