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

With no annotations, the description carries the full burden and it does add useful behavioral context: the tool's behavior switches based on task presence (recommendation vs catalog listing) and the utilities it surfaces are 'paid', implying commercial implications. But it does not explain what a 'best-fit' result looks like, whether invoking the tool itself incurs cost, or what it returns, which are notable gaps for an unannotated tool.

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?

A single sentence of about 22 words carries both usage modes with zero filler. The primary mode (discover best-fit) is front-loaded and the alternate mode (inspect catalog) follows naturally in the same sentence.

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 simple 2-optional-param tool, the description adequately covers the invocation logic and the role of task. However, with no output schema and no annotations, the return shape is undisclosed and budgetUsd remains unexplained, leaving an agent to guess at both the result format and the budget parameter's semantics.

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 explain both parameters. It effectively documents task ('Describe a task in natural language to discover the best-fit...'), but budgetUsd is never mentioned; the schema only states it is a positive number. Without a budgetUsd explanation, an agent cannot tell whether it is a filter, a constraint on recommendations, or something else.

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 action ('discover the best-fit paid AUN utility') plus an alternate mode ('inspect the full utility catalog'), which clearly identifies what the tool does. It implicitly differentiates from the data-processing siblings (entity_normalize, json_repair, text_extract_signals) as the only discovery/catalog tool. However, the loaded term 'paid AUN utility' is unexplained jargon, so it stops short of a 5.

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 is explicit about the two invocation modes: provide a natural-language task to get a recommendation, or omit it to receive the full catalog. This gives clear decision context on how to call the tool. It does not name alternatives or exclusion conditions relative to the sibling tools, but for a self-contained discovery tool that guidance is less critical.

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