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

No annotations are provided, so the description carries full behavioral burden. It discloses the payment model ([x402: 0.003 USDC on Base, pay-per-use]), which is useful context, but says nothing about return format, entity categories, error behavior, or rate limits for an extraction operation.

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?

Very compact and front-loaded: the purpose leads, the input hint and pricing note follow. Every element is short, though the bracketed billing phrase is somewhat tangential to tool selection.

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 extraction tool with no output schema and no annotations, the description covers purpose and input but omits the shape of the returned entities and any operational constraints. 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 coverage is 100% but the schema itself is generic ('service input'), so the description's 'input=texto' adds a marginal clarification that the parameter is text. With only one parameter and high nominal coverage, the baseline of 3 is appropriate.

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 (Extrae) and resource (entidades nombradas) with concrete examples (personas, lugares, orgs), so the agent knows exactly what the tool does. However, it does not distinguish itself from the closely named sibling 'entities' or other extraction tools, leaving overlap unresolved.

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?

There is no when-to-use guidance, no exclusions, and no named alternative (e.g. 'entities' or 'extract-json'). 'input=texto' merely restates the input type rather than telling the agent whether this is the right tool for a given task.

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