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ZatesloFL

Google Workspace MCP Server

by ZatesloFL

create_task_list

Create and manage task lists in Google Workspace. Specify a user’s Google email and task list title to generate a new task list efficiently.

Instructions

Create a new task list.

Args: user_google_email (str): The user's Google email address. Required. title (str): The title of the new task list.

Returns: str: Confirmation message with the new task list ID and details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
user_google_emailYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the 'create_task_list' tool. It creates a new task list in Google Tasks API using the provided title, handles authentication via decorator, error handling, and returns formatted response with new list details. Registered via @server.tool() decorator.
    @server.tool()  # type: ignore
    @require_google_service("tasks", "tasks")  # type: ignore
    @handle_http_errors("create_task_list", service_type="tasks")  # type: ignore
    async def create_task_list(
        service: Resource,
        user_google_email: str,
        title: str
    ) -> str:
        """
        Create a new task list.
    
        Args:
            user_google_email (str): The user's Google email address. Required.
            title (str): The title of the new task list.
    
        Returns:
            str: Confirmation message with the new task list ID and details.
        """
        logger.info(f"[create_task_list] Invoked. Email: '{user_google_email}', Title: '{title}'")
    
        try:
            body = {
                "title": title
            }
    
            result = await asyncio.to_thread(
                service.tasklists().insert(body=body).execute
            )
    
            response = f"""Task List Created for {user_google_email}:
    - Title: {result['title']}
    - ID: {result['id']}
    - Created: {result.get('updated', 'N/A')}
    - Self Link: {result.get('selfLink', 'N/A')}"""
    
            logger.info(f"Created task list '{title}' with ID {result['id']} for {user_google_email}")
            return response
    
        except HttpError as error:
            message = f"API error: {error}. You might need to re-authenticate. LLM: Try 'start_google_auth' with the user's email ({user_google_email}) and service_name='Google Tasks'."
            logger.error(message, exc_info=True)
            raise Exception(message)
        except Exception as e:
            message = f"Unexpected error: {e}."
            logger.exception(message)
            raise Exception(message)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a creation operation (implying mutation) and mentions a return confirmation, but lacks critical details: required permissions, whether this affects existing data, error conditions, or rate limits. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 perfectly structured and concise: a clear purpose statement followed by well-organized Args and Returns sections. Every sentence earns its place, with no redundant information. The front-loaded purpose makes it immediately scannable.

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

Completeness3/5

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

Given this is a mutation tool with no annotations but with an output schema (implied by 'Returns' section), the description is moderately complete. It covers parameters well and indicates return format, but lacks behavioral context about permissions, side effects, or error handling. The presence of an output schema reduces the need to describe return values, but other gaps remain.

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 description explicitly documents both parameters in the Args section, adding meaning beyond the schema which has 0% description coverage. It clarifies that user_google_email is 'required' and identifies it as the user's Google email, and explains title is for the new task list. This fully compensates for the schema's lack of descriptions, though it doesn't provide format examples or constraints.

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 verb 'Create' and resource 'task list', making the purpose immediately understandable. It distinguishes this from sibling tools like 'create_task' (which creates individual tasks) and 'create_task_list' (which creates lists). However, it doesn't explicitly contrast with 'update_task_list' or 'delete_task_list', keeping it from a perfect score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (like needing an existing user account), when not to use it, or how it differs from related tools like 'update_task_list' or 'list_task_lists'. The agent must infer usage from context alone.

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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