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yokan-board
by yokan-board

create_tasks

Add multiple tasks to a specific column in a Yokan Board. Define task details including title, description, and due date to organize work efficiently.

Instructions

Creates multiple new tasks in a specified column.

Args: board_id (int): The ID of the board containing the column. column_id (str): The ID of the column to add the tasks to. tasks (List[Dict]): A list of dictionaries, where each dictionary represents a task and contains 'title' and optional 'description' and 'dueDate'. auth (AuthContext): The authentication context containing user ID and token.

Returns: List[str]: A list of IDs for the newly created tasks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
board_idYes
column_idYes
tasksYes
authYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The 'create_tasks' function serves as the MCP tool handler. It validates the existence of the column and board, generates unique IDs for the new tasks, and appends them to the column's task list.
    async def create_tasks(
        board_id: int,
        column_id: str,
        tasks: List[Dict],
        auth: AuthContext,
    ) -> List[str]:
        """Creates multiple new tasks in a specified column.
    
        Args:
            board_id (int): The ID of the board containing the column.
            column_id (str): The ID of the column to add the tasks to.
            tasks (List[Dict]): A list of dictionaries, where each dictionary represents a task and contains 'title' and optional 'description' and 'dueDate'.
            auth (AuthContext): The authentication context containing user ID and token.
    
        Returns:
            List[str]: A list of IDs for the newly created tasks.
        """
        board = await yokan_client.get_board(board_id=board_id, token=auth.token)
        if "columns" not in board.data or column_id not in board.data["columns"]:
            raise McpError(error=ErrorData(code=NOT_FOUND, message="Column not found"))
    
        column = board.data["columns"][column_id]
        if "tasks" not in column:
            column["tasks"] = []
    
        new_task_ids = []
        for task_data in tasks:
            task_id = str(uuid.uuid4())
            new_task = {
                "id": task_id,
                "content": task_data.get("title"),
                "description": task_data.get("description"),
                "dueDate": task_data.get("dueDate"),
            }
            column["tasks"].append(new_task)
            new_task_ids.append(task_id)
    
        await yokan_client.update_board(
            board_id=board_id, name=board.name, data=board.data, token=auth.token
        )
        return new_task_ids
Behavior2/5

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 states this is a creation operation but doesn't mention permissions needed, rate limits, error conditions, or whether it's idempotent. 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 efficiently structured with a clear opening sentence followed by well-organized Arg and Return sections. Every sentence adds value without redundancy, making it easy to parse and understand.

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 the tool's complexity (mutation with 4 parameters) and no annotations, the description covers parameters well and includes output information. However, it lacks behavioral context (permissions, errors) and sibling differentiation, making it incomplete for optimal agent use despite the output schema.

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?

Schema description coverage is 0%, but the description compensates well by explaining all four parameters: board_id and column_id identify the location, tasks is a list with dictionary structure, and auth provides authentication context. It adds meaningful context beyond the bare schema types.

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 ('Creates') and resource ('multiple new tasks in a specified column'), making the purpose explicit. However, it doesn't distinguish this tool from its sibling 'create_task' (singular), which creates ambiguity about when to use one versus the other.

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?

No guidance is provided on when to use this tool versus alternatives like 'create_task' (singular). The description mentions the column context but doesn't specify prerequisites, constraints, or comparative scenarios with sibling tools.

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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