Calcula el resultado de una calculadora en vivo. Pasá el slug y un objeto inputs con los campos (ver get_calculator). Opcional lang (es|en|pt). Devuelve el resultado calculado en JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | idioma del resultado (default es) | |
| slug | Yes | slug de la calc (ej. calculadora-imc) | |
| inputs | Yes | pares campo→valor, ej. {"peso":80,"altura":180} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'en vivo' (live calculation) but does not disclose whether the tool has side effects, requires authentication, or has rate limits. For a computation tool, it likely is read-only, but that is not stated explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. First sentence states purpose, second sentence details parameters and return type. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must explain return. It states 'returns result in JSON' but does not describe structure or error cases. Given the tool's complexity (nested inputs, three params), more detail would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions. The description adds value by explaining the relationship to get_calculator for inputs, and that lang is optional. This provides context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it calculates a result for a calculator in real-time, requiring a slug and inputs object. It references get_calculator for field definitions, and mentions optional lang. This distinguishes it from siblings get_calculator and search_calculators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells when to use it (compute a result) and how: pass slug and inputs, optionally lang, and references get_calculator for input fields. It lacks explicit when-not-to-use, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: search finds calculators, get_calculator retrieves details, compute runs calculations. No overlap.
All names use verbs and are in snake_case, but compute lacks a noun object unlike get_calculator and search_calculators, causing a slight inconsistency.
Three tools cover the core workflow of search, inspect, and compute, which is well-scoped for a calculator service.
The set covers the complete user-facing lifecycle: finding a calculator, understanding its inputs, and computing a result.