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Glama
JuanDsm04

finanzas-pyme

by JuanDsm04

tendencia_ingresos

Analyze income trends to determine if sales are rising or falling. Get month-over-month change, year-over-year comparison, and a least-squares trend with R² for reliability.

Instructions

Analiza si los ingresos estan creciendo o bajando, con numeros concretos.

Responde "mis ventas van subiendo o bajando?". Entrega tres niveles de evidencia: la variacion contra el mes anterior, la comparacion interanual (que elimina el efecto de la estacionalidad) y una tendencia ajustada por minimos cuadrados con su R2 para indicar que tan confiable es.

Args: mes: Ultimo mes de la ventana de analisis en formato 'YYYY-MM'. Si se omite, se usa el ultimo mes con datos. meses: Tamano de la ventana de analisis, entre 3 y 24 meses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mesNo
mesesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does disclose that the tool returns three levels of evidence (month-over-month, year-over-year, and trend with R^2), but it does not state side effects, permissions, read-only nature, or any potential limitations or errors.

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?

The description is reasonably concise and well-structured, starting with the main purpose, then the response content, followed by parameter details. No unnecessary fluff, though it could be slightly tighter.

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?

The description explains the output content in terms of three evidence levels, which is sufficient given that an output schema exists. It does not describe the exact output format, but that is not required when an output schema is present.

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?

Both parameters are well explained in the description: 'mes' includes format 'YYYY-MM' and default behavior, and 'meses' includes a range (3-24) and default value. This covers all relevant aspects beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool analyzes whether income is rising or falling with concrete numbers, and specifies that it responds to a common user question. It does not explicitly name sibling tools, but the purpose is specific and unambiguous.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus alternatives. The description does not mention conditions, use cases, or comparisons with sibling tools like desglose_gastos or salud_financiera, leaving the agent without explicit selection criteria.

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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