Mapear concepto entre frameworks normativos

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is safe and non-mutating. The description adds valuable behavioral details: partial search capability, expected return format (JSON with mappings), and error handling (empty list with suggestions). No contradictions exist between description and annotations.

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?

The description is moderately long but well-organized with explicit sections (purpose, args, returns, examples, error handling). It is front-loaded with the core purpose and each section contributes useful information. Minor redundancy could be trimmed, but overall it is efficient.

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 there is no output schema, the description fully explains the return JSON format with an example structure. The tool is simple (one parameter) and the description covers purpose, parameters, return, examples, and error handling. Annotations provide safety context. The description is sufficiently complete for an agent to use correctly.

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?

The input schema has 100% description coverage for the single required parameter 'concept', including examples and constraints. The description goes beyond the schema by explaining that searches are partial and accepts both name or ID, and provides additional usage examples, adding meaningful value.

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's purpose: searching for a compliance concept in a 51-concept ontology and showing how it translates across three frameworks (Chile's Law 21.719, ISO/IEC 42001:2023, and the EU AI Act). It uses a specific verb ('Busca') and resource, and the focus on cross-framework mapping distinguishes it from sibling tools like audit text or data flow checking.

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 provides explicit usage context with example questions ('¿cómo se llama en el EU AI Act lo que la Ley 21.719 exige en materia de X?') and concrete use cases. It does not explicitly state when not to use the tool or mention alternatives, but the purpose is clear enough to guide appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.