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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description usefully discloses fallback behavior: 'Si no hay una entrada exacta, devuelve los términos más cercanos,' telling the agent to expect approximate matches rather than errors.

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?

Three sentences, front-loaded with the core action and scope, then usage trigger, then fallback behavior. Every sentence earns its place, though the second sentence is long and slightly dense.

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?

For a read-only, single-parameter lookup with annotations covering the safety profile and no output schema, the description covers what the tool returns (definition, mechanism, company example, related terms) and how it handles misses. Nothing needed to call it correctly is missing.

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% and the single required parameter is documented, so the baseline is 3. The description adds value by naming the domain vocabulary expected (RAG, agente de IA, fine-tuning, embeddings, MCP), broadening the schema's example and clarifying that technology jargon terms are the intended inputs.

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?

States a specific verb ('Define') and resource ('un término de inteligencia artificial... desde el glosario de missyera.com') and enumerates the content of the answer (cómo funciona, ejemplo de empresa, términos relacionados). This clearly distinguishes it from siblings like aprender_datos or cursos_miss_yera, which are learning/course tools rather than dictionary lookups.

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?

'Úsala cuando el usuario pida explicar un término (RAG, agente de IA, fine-tuning, embeddings, MCP)' gives an explicit trigger with concrete example queries. It lacks an explicit 'when not to use' or a named alternative sibling, which keeps it from a 5.

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