MCP in ExitAnty: How an AI Agent Automates Browser Tasks Without Code

Browser automation used to be mostly about code. If someone needed to register an account, fill out a form, upload content, check a dashboard, or collect data from a page, they usually had to write Selenium, Puppeteer, or Playwright scripts.

That worked, but it was inconvenient for operational tasks. A script had to be written, tested, tied to selectors, and then constantly repaired whenever a website changed a button, a field, or the order of steps.

MCP changes the approach. Instead of a rigid script, there is an AI system that controls the browser through normal commands. The user describes the task in natural language, and the agent opens the page, reads the interface, clicks, enters text, fills forms, checks the result, and reports what happened.

In ExitAnty, this works through a real WebKit browser on an iPhone. In other words, the agent does not control an abstract headless browser, but a real mobile browser session inside an ExitAnty profile.

What MCP Means in Simple Terms

MCP, or Model Context Protocol, is a protocol that allows AI models to connect to external tools.

In a regular chat, a model can only respond with text. Through MCP, it receives tools: open a browser, read a page, click a button, fill in a field, take a screenshot, check a URL, or retrieve a list of sessions.

In ExitAnty, the MCP server connects an AI client to the browser on an iPhone. After that, the agent can work with browser profiles in the same way a person would: open a website, inspect the page, make decisions, and perform actions.

The main difference from classic automation is that there is no need to describe every click in code in advance. It is enough to give the task:

Open the working session, go to the required dashboard, check the account status, and tell me what needs attention.

The agent then goes through the steps on its own.

Why This Matters for Multi-Accounting and Operations

Multi-accounting involves many tasks that repeat every day:

• open a profile;
• check account status;
• enter a dashboard;
• fill out a form;
• upload content;
• check notifications;
• update a description;
• collect data;
• take a screenshot;
• record the result.

Previously, these actions were either done manually or automated with separate scripts. But scripts are a poor fit for tasks where the interface can change or where decisions need to be made based on the situation.

An AI agent can receive a more flexible instruction. For example:

Check this account. If there are warnings, list them. If everything is fine, open the settings section and check whether the profile is complete.

This kind of scenario is difficult to describe elegantly with a regular script, but it works well for an agent.

Case 1. Registering Accounts in Services

One of the clearest scenarios is registering an account in a service.

For example, a team may regularly need accounts in a specific service, and their registration can be delegated to AI. Normally, an operator has to:

1. Open the website.
2. Click Sign up.
3. Enter a name, email, and password.
4. Accept the terms.
5. Complete the next onboarding step.
6. Check that the account was created.
7. Save the result.

With MCP, this can be described as a single task:

Open profile "B1", go to the service website, create an account using this data, fill in the required fields, and stop if a CAPTCHA, SMS, 2FA, or manual confirmation appears.

The agent can take prepared data, fill out the form, check errors, and report at which stage the process stopped.

Case 2. Managing Social Media

One of the strongest MCP scenarios is working with social media.

A team can use separate profiles for different brands, projects, clients, or regions. In each profile, the agent can perform repetitive actions:

• open a social network;
• check whether the account is logged in;
• upload a video or image;
• insert a description;
• add hashtags;
• check the preview;
• save a draft;
• publish a post if the process allows it;
• collect statistics for recent publications.

Example task:

Open the brand profile, go to Instagram, check the latest notifications, then upload the prepared post as a draft: add the text, hashtags, and check that the image is displayed correctly.

Or:

Open the project's TikTok profile, upload the video from the prepared folder, insert the description, check the cover, and stop before publishing for manual confirmation.

This approach is especially useful for agencies and SMM teams. The operator prepares the content and rules, while the agent handles the routine inside the interface.

Case 3. Content Planning and Publishing Across Multiple Accounts

When a team has multiple accounts, manual publishing quickly becomes mechanical work.

For example, the same announcement may need to be posted across different profiles, but with small differences:

• another language;
• another region;
• another set of hashtags;
• another publication time;
• another link;
• another caption.

A regular script for this process becomes rigid. An agent can be given an instruction instead:

For each profile in the list, open the corresponding social network, create a publication draft, use the text for the required region, check the preview, and do not publish without confirmation.

The agent can work sequentially or in parallel across multiple sessions if the current configuration supports it.

In practice, this saves time on the most repetitive stages: open the dashboard, find the create-post button, insert text, attach a file, check the preview, and save the draft.

Case 4. Account Warm-Up and Regular Activity

For some working accounts, regular natural activity matters: check the feed, open notifications, reply to comments, update the description, and review messages.

Here, MCP is useful as an assistant for the operator.

Example task:

Open the account, check notifications, list the comments that need a reply, and suggest draft replies. Do not send anything without confirmation.

Or:

Check the brand profile: avatar, description, link, latest publications, and whether the account has any warnings.

This scenario does not require bypassing platform rules. The agent simply helps complete regular manual work faster: look, check, list, and prepare drafts.

Case 5. Checking Accounts and Statuses

Many projects have dozens of accounts across different services. Checking each one manually takes time.

The agent can open a profile, enter the dashboard, and check:

• whether the account is accessible;
• whether there are warnings;
• whether email confirmation is required;
• whether there are new notifications;
• whether the status has changed;
• whether new restrictions appeared;
• whether the profile is complete;
• whether there are errors in the settings.

Example:

Open profile "Account 12", go to the dashboard, check account status, warnings, and access to the main sections. Give a short report at the end.

The result can look like this:

Account 12:
- logged in;
- no warnings;
- profile complete;
- payment section available;
- new notifications: 2.

This is convenient for daily checks, customer support, QA, and operations teams.

Case 6. Working with Marketplaces

Marketplaces are another good example. Seller dashboards contain many repetitive actions:

• check new orders;
• update stock;
• edit a product description;
• upload images;
• check a product card;
• export visible data;
• review buyer messages;
• check moderation status.

Example command:

Open the seller dashboard, check new orders and messages, then go to the products section and list the cards that require moderation or updates.

Or:

Open the product card, compare the title, price, and description with the prepared data, then report what is different.

Here, MCP helps not by replacing business logic, but by removing manual movement through the interface.

Case 7. Parsing Data from Pages

MCP can also be used for careful collection of visible data from pages, if this is allowed by the site's rules.

For example:

• collect product names;
• list prices;
• check whether a purchase button is available;
• compare page data with a spreadsheet;
• collect request statuses;
• read messages in a dashboard;
• create a short summary of a page.

Example task:

Open the product page and list the name, price, availability, delivery options, and warnings, if any.

Or:

Open the request list and collect request numbers, statuses, and last update dates into a short table.

The difference from regular parsing is that the agent works through a browser session and sees the page the way the user sees it.

Case 8. Checking Ad Dashboards

In advertising and analytics dashboards, the task is often simply to log in and check the current state:

• whether campaigns are active;
• whether there are rejected ads;
• whether the budget has run out;
• whether warnings appeared;
• which metrics changed;
• whether the platform has new recommendations.

Example:

Open the ad dashboard, check active campaigns, list ads with errors or warnings, and make a short status summary.

This is convenient for daily monitoring when no one wants to manually open every dashboard and search for changes.

Real Case

We recorded a short video showing the full ExitAnty MCP workflow on a real example: an AI agent goes through Reddit account registration from start to finish.

The demo shows the whole process: the agent opens a browser session, prepares a test email, goes to Reddit, fills out the registration form, completes the required steps, and brings the scenario to a created account.

This is a good example of how MCP turns browser routine into a natural-language task: instead of manual clicks or a separate script, the user describes the goal, and the agent performs the actions inside an ExitAnty profile.

You can watch the video on our YouTube or in the Telegram channel:

Why This Is More Convenient Than Regular Scripts

A script works well when the process is fully predictable. But real services often behave differently:

• a button moved;
• a new banner appeared;
• the site asked to confirm an action;
• the form changed;
• some fields became optional;
• the interface differs by region;
• the account landed on a different screen after login.

A rigid script breaks in this situation. An agent can read the current state of the page and choose the next step.

For example, instead of the instruction "click the `.submit-button` selector", the agent receives the task:

Find the button that continues registration and click it if the form is filled in correctly.

This makes automation closer to the way a real operator works.

Installation

Many AI agents are supported: Codex, Claude Code, Ollama, and others.

Installation instructions for different AI systems are available in our GitHub repository:

Conclusion

ExitAnty MCP turns an anti-detect browser from a tool for working with profiles into a full platform for browser automation.

An AI agent can do more than open websites and perform individual clicks. It can take over full workflows: account registration and onboarding, publication preparation, social media management, dashboard checks, data collection, QA, and other repetitive processes. In some tasks, this already replaces separate professional tools for operations, content uploading, profile management, and account control.

The main advantage over regular headless browsers is that the agent works inside a full WebKit session on an iPhone. The profile keeps cookies, fingerprint, proxy, local state, and the behavior of a real browser. Because of this consistency, automation looks closer to normal user activity rather than a separate server-side script.

That is why ExitAnty MCP should be seen not just as an interesting feature, but as a new automation layer on top of an anti-detect browser. It reduces dependence on code, speeds up routine processes, and makes working with multiple profiles much more flexible.