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add_dashboard_element

Add a visualization tile to a dashboard by defining a query or embedding a saved Look.

Instructions

Add a visualization tile to a dashboard. Requires a query definition (model, view, fields) or a saved Look ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dashboard_idYesID of the dashboard
titleYesTitle for the tile
typeNoElement type: 'vis' (visualization), 'text', 'filter'vis
look_idNoID of a saved Look to embed
query_modelNoLookML model for an inline query
query_viewNoExplore/view for an inline query
query_fieldsNoFields for an inline query

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description must disclose behavioral traits. It reveals the requirement for either a query definition or a saved Look ID, which is helpful. However, it does not describe side effects (e.g., whether existing tiles are affected), error behavior, or permission requirements, limiting transparency for a tool that modifies a dashboard.

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 extremely concise: two sentences with no unnecessary words. The core purpose is front-loaded, and every sentence adds critical information (purpose and requirements). This is an exemplar of conciseness.

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?

Given that there are 7 parameters with 2 required, no annotations, but an output schema exists, the description adequately covers the essential usage. It misses details like whether text or filter types require additional inputs, or what happens on failure, but it is sufficient for an agent to understand how to invoke the tool for the primary use case.

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 input schema has 100% coverage with descriptions for all 7 parameters. The description adds the constraint that either a query definition (model, view, fields) or a look_id must be provided, but this is already implied by the schema's nullable defaults and descriptions. Thus, the description adds minimal value beyond the schema.

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 states explicitly that the tool adds a visualization tile to a dashboard, and specifies the two ways to define content (query definition or saved Look ID). This clearly differentiates it from sibling tools like add_dashboard_filter, which adds a filter, and create_dashboard, which creates the dashboard itself.

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 implies when to use the tool (when you want to add a tile to a dashboard), but does not explicitly state when not to use it or contrast with alternatives like create_look or run_dashboard. No exclusions or alternatives are mentioned, leaving the agent to infer usage context.

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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