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vincenthopf

Gemini Web Automation MCP

by vincenthopf

list_web_tasks

Track and monitor all active and completed web browsing automation tasks in your current session. View task summaries to manage multiple concurrent operations.

Instructions

List all web browsing tasks, including active and completed ones.

Shows a summary of all tasks in the current session. Useful for tracking
multiple concurrent browsing operations.

Returns:
    Dictionary containing:
    - ok: Boolean indicating success
    - tasks: Array of task status objects (compact format)
    - count: Total number of tasks
    - active_count: Number of currently running tasks

Examples:
    - list_web_tasks()

Note: Returns compact task summaries. Use check_web_task(task_id) for details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'list_web_tasks' tool. It is decorated with @mcp.tool() for automatic registration in FastMCP. The function lists all web tasks by calling task_manager.list_tasks(), computes active count, and returns a summary dictionary.
    async def list_web_tasks() -> dict[str, Any]:
        """
        List all web browsing tasks, including active and completed ones.
    
        Shows a summary of all tasks in the current session. Useful for tracking
        multiple concurrent browsing operations.
    
        Returns:
            Dictionary containing:
            - ok: Boolean indicating success
            - tasks: Array of task status objects (compact format)
            - count: Total number of tasks
            - active_count: Number of currently running tasks
    
        Examples:
            - list_web_tasks()
    
        Note: Returns compact task summaries. Use check_web_task(task_id) for details.
        """
        logger.info("Listing all web tasks")
    
        tasks = task_manager.list_tasks()
        active_count = sum(1 for t in tasks if t.get("status") in ["pending", "running"])
    
        return {
            "ok": True,
            "tasks": tasks,
            "count": len(tasks),
            "active_count": active_count
        }
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail, including specific keys like 'ok,' 'tasks,' 'count,' and 'active_count,' which adds value beyond basic listing. However, it doesn't mention potential limitations such as rate limits, session dependencies, or error handling, leaving some behavioral aspects unclear.

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

Conciseness4/5

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

The description is well-structured and front-loaded with the core purpose. It uses bullet points for the return values and includes a note and example, which are helpful. However, some sentences could be more concise, such as 'Shows a summary of all tasks in the current session,' which slightly repeats the initial statement, but overall it's efficient and informative.

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?

Given the tool's low complexity (0 parameters) and the presence of an output schema (implied by the detailed return description), the description is quite complete. It explains the purpose, usage, return format, and provides an example and alternative tool. The only minor gap is the lack of explicit behavioral constraints like error cases, but this is mitigated by the output details.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate here. Since there are no parameters, the baseline is 4, as the description doesn't need to compensate for any gaps and focuses on output semantics instead.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'List all web browsing tasks, including active and completed ones.' It specifies the verb ('List') and resource ('web browsing tasks'), and distinguishes it from siblings by mentioning 'compact task summaries' versus 'check_web_task(task_id) for details.' However, it doesn't explicitly differentiate from other list-like siblings if any existed, but since none are present, it's clear.

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 provides clear context on when to use this tool: 'Shows a summary of all tasks in the current session. Useful for tracking multiple concurrent browsing operations.' It also gives an explicit alternative: 'Use check_web_task(task_id) for details.' However, it lacks explicit exclusions or comparisons to other siblings like 'browse_web' or 'start_web_task,' which could be helpful but isn't critical here.

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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