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

With no annotations, the description carries the burden of behavioral disclosure. It does usefully note that output is JSON and that the service is pay-per-use with a cost in USDC on Base. Yet it does not mention whether the operation is safe/read-only, whether costly surprises or rate limits exist, or any other consequential behavior.

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 ('Named-entity recognition') occupies the beginning, followed by the output type and then a usage hint plus payment note. Every element contributes to either knowing what it does or how to call it, with no fluff.

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?

The tool is simple—one input parameter, no nested objects, no output schema—so the description does not need much to invoke it correctly. Still, it only says 'as JSON'; it fails to indicate the structure of the returned entities (e.g., an array of entity names with types and offsets). For a tool with no output schema, this is a notable gap.

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% because the only parameter 'input' has a description 'texto'—the description's 'input=text' merely repeats the same concept. No deeper parameter semantics, such as encoding, language support, or size limits, are added.

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 the specific task 'Named-entity recognition (NER)' and the input/output ('input=text', 'as JSON'). This makes it clearly distinguishable from sibling tools like sentiment or keywords, since NER is a unique, well-defined functionality.

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 shows the basic expected input ('input=text') and the output format, so an agent can infer when to use it. However, it gives no explicit conditions, alternatives, exclusion scenarios, or prerequisite details beyond the trivial 'provide text.'

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