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yokan-board
by yokan-board

update_board

Modify the name of an existing Kanban board in the Yokan Board system to reflect organizational changes or corrections.

Instructions

Updates the name of a Kanban board.

Args: board_id (int): The ID of the board to update. name (str): The new name for the board. auth (AuthContext): The authentication context containing user ID and token.

Returns: int: The ID of the updated board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
board_idYes
nameYes
authYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler function for "update_board", which orchestrates the board update process by fetching the existing board and calling the yokan_client.update_board method.
    async def update_board(
        board_id: int,
        name: str,
        auth: AuthContext,
    ) -> int:
        """Updates the name of a Kanban board.
    
        Args:
            board_id (int): The ID of the board to update.
            name (str): The new name for the board.
            auth (AuthContext): The authentication context containing user ID and token.
    
        Returns:
            int: The ID of the updated board.
        """
        # For now, we only support updating the name.
        # The `data` field will be fetched from the existing board.
        board = await yokan_client.get_board(board_id=board_id, token=auth.token)
        return await yokan_client.update_board(
            board_id=board_id, name=name, data=board.data, token=auth.token
        )
  • The actual low-level client method that sends the PUT request to the Yokan API to update a board.
    async def update_board(
        self, board_id: int, name: str, data: Dict[str, Any], token: str
    ) -> int:
        headers = self._get_auth_headers(token)
        json_data = {"name": name, "data": data}
        response = await self.client.put(
            f"{self.base_url}/boards/{board_id}", headers=headers, json=json_data
        )
        response.raise_for_status()
        return response.json()["changes"]
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Updates' implies a mutation, the description lacks details on permissions (e.g., who can update), side effects (e.g., if it affects other board properties), error handling, or rate limits. It only states the basic action without behavioral context.

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. The 'Args' and 'Returns' sections are organized, but the 'auth' parameter explanation could be more specific (e.g., 'required for authentication'). Overall, it's efficient with minimal waste.

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 mutation nature, 0% schema coverage, and no annotations, the description is moderately complete. It covers parameters and return value, but lacks behavioral details like error cases or permissions. The output schema exists, so return values are documented, but overall context for safe use is insufficient.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It lists all three parameters (board_id, name, auth) with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide format details (e.g., name length limits) or deeper context for 'auth', leaving gaps in parameter understanding.

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: 'Updates the name of a Kanban board.' It specifies the verb ('Updates') and resource ('name of a Kanban board'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'update_column' or 'update_task', which would require a 5.

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 (e.g., needing an existing board), exclusions, or comparisons to siblings like 'update_column' or 'update_task'. This leaves the agent without context for tool selection.

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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