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voducdan

metabase-mcp

by voducdan

update_dashboard

Update dashboard properties: name, description, collection, and filter parameters. Archive or unarchive dashboards to control visibility.

Instructions

Update properties of a dashboard, including its parameter/filter list.

Use parameters to define dashboard-level filters. Each parameter is a dict with keys like id (string, required — a stable identifier), name, slug, type (e.g. "date/all-options", "date/single", "date/range", "category", "string/=", "number/="), and optional default, sectionId, values_source_type, values_source_config.

Args: dashboard_id: The ID of the dashboard to update. name: New name for the dashboard. description: New description for the dashboard. collection_id: Move the dashboard to a different collection. parameters: Full replacement list of dashboard parameters. archived: Set true to archive, false to unarchive.

Returns: The updated dashboard object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dashboard_idYes
nameNo
descriptionNo
collection_idNo
parametersNo
archivedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Tool handler for update_dashboard. Updates properties of a Metabase dashboard including name, description, collection_id, parameters, and archived status via PUT request to /api/dashboard/{dashboard_id}.
    @mcp.tool
    async def update_dashboard(
        dashboard_id: int,
        ctx: Context,
        name: str | None = None,
        description: str | None = None,
        collection_id: int | None = None,
        parameters: list[dict[str, Any]] | None = None,
        archived: bool | None = None,
    ) -> dict[str, Any]:
        """
        Update properties of a dashboard, including its parameter/filter list.
    
        Use `parameters` to define dashboard-level filters. Each parameter is a dict
        with keys like `id` (string, required — a stable identifier), `name`,
        `slug`, `type` (e.g. "date/all-options", "date/single", "date/range",
        "category", "string/=", "number/="), and optional `default`, `sectionId`,
        `values_source_type`, `values_source_config`.
    
        Args:
            dashboard_id: The ID of the dashboard to update.
            name: New name for the dashboard.
            description: New description for the dashboard.
            collection_id: Move the dashboard to a different collection.
            parameters: Full replacement list of dashboard parameters.
            archived: Set true to archive, false to unarchive.
    
        Returns:
            The updated dashboard object.
        """
        payload: dict[str, Any] = {}
        if name is not None:
            payload["name"] = name
        if description is not None:
            payload["description"] = description
        if collection_id is not None:
            payload["collection_id"] = collection_id
        if parameters is not None:
            payload["parameters"] = parameters
        if archived is not None:
            payload["archived"] = archived
    
        if not payload:
            raise ToolError("No update fields provided. Specify at least one field to update.")
    
        try:
            await ctx.info(f"Updating dashboard {dashboard_id}")
            result = await metabase_client.request(
                "PUT", f"/dashboard/{dashboard_id}", json=payload
            )
            await ctx.info(f"Successfully updated dashboard {dashboard_id}")
            return result
        except Exception as e:
            error_msg = f"Error updating dashboard {dashboard_id}: {e}"
            await ctx.error(error_msg)
            raise ToolError(error_msg) from e
  • server.py:949-950 (registration)
    The @mcp.tool decorator registers this async function as an MCP tool named 'update_dashboard'.
    @mcp.tool
    async def update_dashboard(
Behavior4/5

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

Without annotations, the description carries the full burden. It discloses that parameters undergo a full replacement ('Full replacement list') and for archived: 'Set true to archive, false to unarchive.' It also specifies the return value. However, it does not mention authorization needs or whether other fields are partially updated.

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 with a summary sentence, a detailed block about parameters, an Args list, and a Returns line. It is front-loaded with the main purpose, though the parameter explanation is somewhat lengthy but justified by complexity.

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 all six parameters and the return value. Given the complexity and presence of an output schema, it provides sufficient detail. It does not cover error cases or permissions, but that is acceptable for this context.

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?

Schema description coverage is 0%, so the description adds crucial meaning. It explains each parameter in the Args block, including the detailed structure of the parameters dict with allowed types and optional keys. This goes well beyond the bare schema types.

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 'Update properties of a dashboard', listing specific properties such as name, description, collection_id, parameters, and archived. This distinguishes it from sibling tools like create_dashboard (create) and list_dashboards (list).

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

Usage Guidelines3/5

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

The description explains how to use the parameters field in detail, but does not explicitly state when to use this tool versus alternatives like add_card_to_dashboard or create_dashboard. It lacks explicit when-to-use or when-not-to-use guidance.

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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