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hotmart_sales_dashboard_app

Visualizes Hotmart sales data with revenue, average ticket, and order count. Displays daily sales line chart, payment type pie chart, and data table for the selected period.

Instructions

Painel visual de vendas Hotmart no período.

Cards de métricas (receita total, ticket médio, qtd) + LineChart vendas/dia + PieChart por payment_type + DataTable. Use quando o usuário pedir 'dashboard de vendas', 'painel de vendas', 'visão geral das vendas do mês'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateNoStart date. Unix timestamp in milliseconds.
end_dateNoEnd date. Unix timestamp in milliseconds.
product_idNoOptional product_id filter.
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It describes the output components but does not state whether the operation is read-only, if authentication is required, or what happens when no parameters are given (default period). The lack of any mutating or destructive hint leaves ambiguity.

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 three sentences: first introduces the dashboard, second lists components, third gives usage triggers. It is front-loaded and every sentence contributes essential information without waste.

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 the many sibling dashboard tools, the description adequately explains the visual output (cards, charts, table). It does not detail default behavior for date range or pagination, but the schema covers parameters. No output schema exists, but the description compensates by listing output elements.

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?

Schema coverage is 100% with clear descriptions for each parameter (start_date, end_date, product_id). The tool description adds no additional meaning beyond what the schema already provides. Baseline of 3 applies since description does not compensate beyond 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 clearly specifies the tool shows a visual sales dashboard with metric cards (revenue, average ticket, quantity), line chart, pie chart, and data table. It identifies the resource ('vendas' - sales) and the verb is implied (dashboard/display). This distinguishes it from sibling dashboards like commissions or events.

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 explicitly lists user phrases that trigger this tool ('dashboard de vendas', 'painel de vendas', 'visão geral das vendas do mês'). This provides clear context for when to use. However, it does not mention when not to use or suggest alternative tools for more detailed breakdowns.

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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