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CW-Codewalnut

Metabase MCP Server

get_dashboard_cards

Retrieve all cards from a specific Metabase dashboard to analyze or manage its visualizations and data components.

Instructions

Get cards in a dashboard.

Args: dashboard_id (int): ID of the dashboard.

Returns: Dict[str, Any]: Cards in the dashboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dashboard_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The RequestMethod enum defines the HTTP methods (GET, POST, PUT, DELETE) used by the helper function. The get_dashboard_cards tool uses RequestMethod.GET to specify the HTTP method for the API request.
    class RequestMethod(Enum):
        GET = auto()
        POST = auto()
        PUT = auto()
        DELETE = auto()
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. It states the tool 'Get cards in a dashboard,' which implies a read-only operation, but doesn't clarify aspects like authentication requirements, rate limits, error handling, or what 'cards' entail (e.g., metadata, content, or both). This leaves significant gaps for safe and effective use.

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 well-structured and concise, with a clear purpose statement followed by separate 'Args' and 'Returns' sections. Every sentence serves a purpose without redundancy, making it easy to parse and understand quickly.

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 low complexity (1 parameter) and the presence of an output schema (implied by 'Returns: Dict[str, Any]'), the description is somewhat complete. However, with no annotations and minimal behavioral details, it falls short of providing full context for reliable use, especially compared to sibling tools that might offer similar functionality.

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?

The description includes an 'Args' section that explains the 'dashboard_id' parameter as 'ID of the dashboard,' adding semantic meaning beyond the schema's title 'Dashboard Id' and type 'integer.' Since schema description coverage is 0%, this compensates partially, but it's minimal and doesn't elaborate on format or constraints (e.g., valid ranges).

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: 'Get cards in a dashboard.' This specifies the verb ('Get') and resource ('cards in a dashboard'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this from sibling tools like 'get_dashboard_items' or 'get_metabase_cards', which might have overlapping functionality.

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. With sibling tools such as 'get_dashboard_items' and 'get_metabase_cards' available, there's no indication of how this tool differs in context, scope, or use cases, leaving the agent to guess based on names 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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