explain_sae_table

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

With no annotations provided, the description carries the full burden, and it does a good job by explicitly framing the tool as explanatory rather than data-mutating: it describes schema, business purpose, key fields, and relationships, not row data. It does not disclose error behavior or unsupported table handling, but for a non-destructive metadata-explanation tool the key behavioral traits are communicated.

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 compact and front-loaded: the main purpose comes first, followed by a practical use-case sentence and a minimal parameter definition. There is no fluff or repetition, and the example list earns its place by making the parameter concrete.

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 the tool has only one required parameter and an output schema, the description is nearly complete: it names what the output covers and what values table_name should take. A small gap is the lack of explicit guidance on unsupported table names or case sensitivity, but this is minor for a low-complexity explanatory tool.

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 only provides a title ('Table Name') with 0% schema description coverage, so the parameter description must compensate. The description defines table_name as 'Nombre de la tabla' and gives concrete examples ('MULT', 'INVE', 'ALMACENES', 'MINVE', 'FACTF'), which is sufficient for an agent to correctly supply the single required argument. It does not enumerate all valid values, but the examples plus 'etc.' give adequate guidance.

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 states a specific verb ('Explica') and a clear resource ('la estructura, propósito de negocio, campos clave y relaciones de una tabla de Aspel SAE'), including concrete examples like MULT, INVE, and FACTF. This clearly distinguishes it from the sibling data-retrieval tools such as get_stock_by_warehouse or search_products, which operate on data rather than explaining table metadata.

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 phrase 'Útil para comprender tablas...' provides clear context for when to use the tool: when an agent needs to understand the structure, business purpose, or relationships of an Aspel SAE table. It does not explicitly name alternatives or state when not to use it, so it misses the full when/when-not guidance, but the intended use is unambiguous.

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