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set_visual_interactions

Define how selecting a data point in one visual filters or highlights another visual in your Power BI report.

Instructions

Set how a SOURCE visual affects a TARGET visual when a data point is selected (edit interactions). interaction = filter | highlight | none. sourceVisual/targetVisual are visual names (from list_visuals). Writes a { source, target, type } override in the page config (type: 1=filter, 2=highlight, 3=none). Verify the render in Desktop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYespage name or displayName
interactionYesfilter | highlight | none
sourceVisualYesthe visual whose selections drive the interaction
targetVisualYesthe visual that is affected
reportSessionIdYes
Behavior4/5

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

With no annotations, the description takes on the full burden of explaining side effects. It explicitly states that it 'Writes a { source, target, type } override in the page config' and provides the numeric type mapping. It also advises to 'Verify the render in Desktop,' adding practical behavioral context. It doesn't cover permission requirements or reversibility, but the key behavioral trait is disclosed.

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 compact—three sentences—and every sentence provides valuable information. It front-loads the core purpose, then adds parameter details and a verification tip. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description fully covers what the tool does, how to use it, what parameters mean, and the side effect (write config override). It even gives a practical follow-up ('Verify the render in Desktop'). This is complete for a tool of this complexity.

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?

The description adds meaningful semantics beyond the schema: it explains the allowed values for interaction (filter | highlight | none), the mapping to numeric types, and the origin/purpose of sourceVisual/targetVisual (visual names from list_visuals). This is especially useful despite the schema already covering 80% of parameters.

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 uses a specific verb+resource: 'Set how a SOURCE visual affects a TARGET visual when a data point is selected.' This clearly distinguishes it from other visual-related tools like set_visual_property or set_visual_format, which target different aspects.

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

Usage Guidelines4/5

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

The description implies when to use the tool by specifying the exact use case (editing interactions). It also tells users that source/target visuals come from list_visuals. However, it does not explicitly mention alternatives or exclusions, so it stops short of full 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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