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Generate a Before/After or A/B Comparison Chart

meta_generate_comparison_chart
Idempotent

Create a side-by-side bar chart to compare metrics between two groups, such as time periods or campaign variants, for clear visual analysis.

Instructions

Generates a side-by-side comparison chart — perfect for comparing two time periods, two campaigns, or A/B test results.

Args:

  • title (string): Chart title

  • metrics (string[]): Metric names (y-axis labels)

  • group_a (object): { label: string, values: number[], color?: string }

  • group_b (object): { label: string, values: number[], color?: string }

  • chart_type (string): 'bar' or 'horizontalBar' (default: 'horizontalBar')

  • width (number): Width in pixels (default: 800)

  • height (number): Height in pixels (default: 400)

  • output_path (string, optional): Save PNG to this path

Example — comparing two weeks: title: "This Week vs Last Week" metrics: ["Impressions", "Reach", "Clicks", "Spend"] group_a: { label: "Last Week", values: [12000, 8000, 450, 150] } group_b: { label: "This Week", values: [15000, 10500, 620, 185] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
metricsYesMetric names (y-axis labels)
group_aYes
group_bYes
chart_typeNohorizontalBar
widthNo
heightNo
output_pathNo
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior4/5

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

Adds detail beyond annotations: describes chart type defaults, optional output path for saving PNG, and example inputs. Annotations already indicate idempotent and open world; description adds behavioral context without contradiction.

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?

Well-organized with an Args section and example. Front-loads purpose, every sentence adds value. No fluff.

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 9 parameters, nested objects, and no output schema, the description is complete: it documents all parameters, provides defaults, and an example. No missing information for a chart generation tool.

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?

With only 22% schema description coverage, the description compensates fully by listing all parameters with types, defaults, and a detailed example explaining the meaning of group_a and group_b objects.

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 it generates a side-by-side comparison chart, with specific use cases (comparing time periods, campaigns, A/B tests). It distinguishes itself from the sibling 'meta_generate_chart' by focusing on comparison scenarios.

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?

Provides clear context on when to use (comparing two periods, campaigns, A/B tests). Does not explicitly mention when not to use or alternatives, but the context is sufficient.

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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