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

With no annotations, the description carries the full burden and does disclose output content (percentiles 10-90, data level, confidence, sample size) plus the data-source fallback logic, which is real behavioral context. It does not mention auth requirements, rate limits, or whether missing-data cases return empty vs error, leaving some gaps.

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 tight sentences, front-loaded with what is returned and followed by the filtering behavior. No redundant restatement of the title; both sentences earn their place.

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?

An output schema exists so return values need not be spelled out, and the two-parameter surface is small. The description covers both parameters' semantics and the fallback behavior; the main omission is the safety/read-vs-write profile, which no annotation supplies.

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 description coverage is 0%, so the description must compensate and largely does: it clarifies codigo_producto is a UN code ("código ONU") and explains exactly what organismo_id does via the fallback rule. Syntax/format details for the code are still absent, but semantic meaning is conveyed for both parameters.

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 resource and scope: historical price bands (percentiles 10-90), data level, confidence and sample size for a product identified by UN code. This is clearly a price-lookup tool and is distinguishable in subject matter from siblings like buscar_productos or perfil_de_comprador, though it does not name or contrast them explicitly.

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 second sentence gives useful behavioral guidance (use organismo data first, fall back to sector/country when insufficient), but there is no explicit when-to-use / when-not-to-use statement and no routing against the sibling tools. Usage is implied rather than prescribed.

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