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dlwjdtn535

mcp-chrome-server

tool_web_login

Automates website login by filling credentials and handling CAPTCHA detection. Supports form, OAuth, and API authentication with special handling for Naver login.

Instructions

    Perform website login

    This tool attempts to log in to the provided URL. It waits for automatic
    input prevention (CAPTCHA) to be detected before allowing the user to manually resolve it.

    Args:
        url (str): Login page URL
        credentials (Dict[str, str]): Login credentials
            - username: User ID
            - password: Password
        selectors (Dict[str, str]): CSS selectors for login form elements
            - username: ID input field selector
            - password: Password input field selector
            - submit: Login button selector
        auth_type (str, optional): Authentication type. Defaults to "form"
            - form: Regular form-based login
            - oauth: OAuth authentication
            - api: API-based authentication
        wait_for (Optional[str], optional): CSS selector for element to wait for after login

    Returns:
        Dict[str, Any]: Login result
            - success (bool): Login success status
            - message (str): Status message
            - session_data (Dict): Login success session information
                - cookies: Cookie information
                - localStorage: Local storage data
                - sessionStorage: Session storage data

    Special handling:
        1. CAPTCHA detection
            - If reCAPTCHA or standard CAPTCHA is detected, wait for user to manually resolve
            - Automatically continue login process after resolution
        2. Naver login
            - Automatic detection of various special situations like automatic input prevention, 2-step authentication
            - Detailed error message analysis and return

    Example:
        >>> result = tool_web_login(
        ...     url="https://nid.naver.com/nidlogin.login",
        ...     credentials={
        ...         "username": "your_username",
        ...         "password": "your_password"
        ...     },
        ...     selectors={
        ...         "username": "#id",
        ...         "password": "#pw",
        ...         "submit": ".btn_login"
        ...     }
        ... )
        >>> if not result["success"] and "CAPTCHA" in result["message"]:
        ...     # Wait for user input for CAPTCHA resolution
        ...     print("Please solve the CAPTCHA in the browser")
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
credentialsYes
selectorsYes
auth_typeNoform
wait_forNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behaviors: it attempts login, waits for CAPTCHA auto-detection, and continues after user resolution. It also mentions Naver-specific handling. However, it does not cover failure modes like incorrect credentials or network issues.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for overview, parameters, returns, special handling, and an example. Every sentence is informative and there is no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, parameters, return values, and special handling for CAPTCHA and Naver. It does not mention page navigation or explicit waiting behavior, but given the complexity, it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully explains each parameter: url, credentials (with subfields), selectors (with subfields), auth_type (with options and default), and wait_for (purpose). This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Perform website login' and explains the tool's purpose. It distinguishes itself from sibling browser automation tools by focusing solely on login functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (for logging into websites) and mentions special cases like CAPTCHA and Naver login. It does not explicitly state when not to use it, but the context and sibling list make its primary use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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