Devuelve la ficha de una calculadora por slug: qué inputs toma (id, tipo, requerido, valores válidos) y una URL de ejemplo. Usalo antes de compute para saber qué parámetros pasar.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | slug de la calc (ej. calculadora-imc) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the return content (inputs, example URL) but does not mention side effects, permissions, or response format. Adequate but not comprehensive.
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 in Spanish, concise and directly to the point. No unnecessary information.
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?
Given low complexity (1 param, no output schema, no annotations), the description adequately covers purpose and usage. However, it lacks details about the output structure (e.g., JSON format) which could be useful for an AI agent.
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 a single parameter 'slug' and its description. The tool description adds context by explaining what the tool returns about the calculator, but does not add significant meaning 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 the tool returns a calculator details by slug, listing what it provides (inputs, example URL). It distinguishes from the sibling tool 'compute' by indicating its use before compute.
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?
Explicitly says 'Usalo antes de compute para saber qué parámetros pasar.' This directly tells when to use it. It does not mention the other sibling 'search_calculators', but the purpose implies usage when slug is known.
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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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.