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

With no annotations, the description carries the full burden, but it does disclose genuinely useful behavioral context: a per-use cost of 0.005 USDC on Base via x402. It omits latency, rate limits, and output format, but the payment/cost disclosure is real added value beyond a bare description.

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 compact sentences with the purpose front-loaded and the pricing note bracketed at the end. No wasted words, though the mixed Spanish/English fragments read as terse rather than polished.

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 single-parameter tool with no output schema and no annotations, the definition covers the basic call shape and cost but says nothing about what the inference returns or expected input length/format. Adequate but with clear gaps.

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 100% (single 'input' string documented as 'service input'), so the schema already does the lifting. The description maps 'input' to 'tu consulta', which is marginally clarifying but adds little beyond the schema's own wording. Baseline 3 applies.

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 recognizable action (inference) and names the backing models (DeepSeek/Kimi), which is more than a tautology. However, it offers no differentiation from near-identical siblings such as 'inference', 'inferencia', and 'ai-inference', so an agent cannot tell these apart from the text alone.

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 says only 'input=tu consulta' with no when-to-use context, no prerequisites, and no reference to the many sibling inference tools. It provides no guidance on selecting this tool over alternatives.

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