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dponcedeleonf

demo-app-mcp-prestamo

capturar_monto_y_fecha

Captures the loan amount in soles and the preferred monthly payment day, then calculates three repayment plans (12, 24, and 36 installments) with monthly fees, total interest, and TCEA.

Instructions

Registra el monto (en soles) y el día del mes para el pago (1 a 28) de una solicitud de préstamo que ya se inició con iniciar_solicitud_prestamo. Con esos dos datos, el servidor calcula tres planes de pago (12, 24 y 36 cuotas, cada uno con su cuota mensual, intereses totales y TCEA) y el widget avanza al paso donde el usuario elige uno de esos planes.

Normalmente la llama el widget cuando el usuario envía el formulario del primer paso. Tú también puedes llamarla si el usuario te da los dos valores directamente en el chat (por ejemplo, 'quiero 3500 soles y que me cobren el día 15').

Te devuelve un mensaje con los tres planes ya calculados, para que puedas comentarlos si el usuario te pregunta antes de elegir.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
montoYesMonto solicitado en soles.
dia_pagoYesDía del mes para el débito.
Behavior4/5

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

With no annotations, the description carries full behavioral burden. It discloses that the tool registers data (state-changing), triggers server-side calculation of three plans, advances the widget to the next step, and returns a message with the calculated plans. This provides a clear picture of the side effects and return value, though it does not mention error handling or state reversibility.

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 well-structured and front-loaded with the core action ('Registra...'). Each sentence adds value: purpose, consequences, usage context, and return behavior. It is appropriately concise for the tool's complexity and contains no redundant fluff.

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 absence of an output schema and annotations, the description effectively explains the full behavior, including the return value and when to use it. It covers the prerequisite and subsequent flow, making it nearly complete for an agent. Minor gaps include lack of error behavior specs, but these are mitigated by schema constraints.

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% (both parameters have descriptions in the schema), so the baseline is 3. The description reinforces the parameter meaning (amount in soles, day 1-28) and gives a concrete example, but does not add materially new semantic information beyond what the schema already provides.

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 the tool captures the amount (in soles) and payment day (1-28) for an existing loan request initiated with `iniciar_solicitud_prestamo`. It also explains the result (calculation of three payment plans) and its role in the flow, distinguishing it from sibling tools by referencing the prerequisite and next step.

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 gives explicit context on when to call: normally the widget calls it after form submission, and the AI can call it directly when the user provides both values in chat. It also states a prerequisite (loan must already be initiated), which acts as a when-not condition. However, it does not explicitly name alternative tools or exclusions beyond the prerequisite.

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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