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Simulação do Simples Nacional (Anexo III vs V)

simulate_simples_nacional
Read-onlyIdempotent

Simulador completo do Simples Nacional para empresas de tecnologia. Compara Anexo III vs Anexo V, calcula alíquota efetiva, imposto mensal estimado, economia anual potencial e emite alertas de risco sobre o Fator R. Use para planejamento tributário de empresas no regime Simples Nacional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rbt12YesReceita Bruta Total acumulada nos últimos 12 meses em R$ (ex: 480000)
folha12YesFolha de pagamento acumulada nos últimos 12 meses em R$ (inclui pró-labore + salários + INSS)
faturamento_mesYesFaturamento em R$ do mês atual a ser tributado

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "content": {
      +      "items": {
      +        "properties": {
      +          "text": {
      +            "description": "JSON serializado com o resultado da simulação tributária",
      +            "type": "string"
      +          },
      +          "type": {
      +            "description": "Tipo do conteúdo (ex: \"text\")",
      +            "type": "string"
      +          }
      +        },
      +        "required": [
      +          "type",
      +          "text"
      +        ],
      +        "type": "object"
      +      },
      +      "type": "array"
      +    }
      +  },
      +  "required": [
      +    "content"
      +  ],
      +  "type": "object"
      +}
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, non-destructive. The description adds that it is a comprehensive simulation, includes comparisons, calculations, and risk alerts, providing behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, packs all key information, but could be more concise by splitting. Still efficient.

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 complexity, description covers tool purpose and outputs effectively. Output schema handles return values. Adequate for decision-making.

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%, so baseline is 3. The description does not add additional meaning beyond the schema's parameter descriptions.

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 explicitly states the tool simulates Simples Nacional for tech companies, compares Annex III vs V, calculates effective rate, monthly tax, potential annual savings, and issues risk alerts. It clearly distinguishes from siblings like calculate_fator_r.

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?

Directly states to use for tax planning of companies under Simples Nacional regime. While it doesn't explicitly mention when not to use, the context with siblings provides implicit differentiation.

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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