x402-is-ugly-number
Is Ugly Number: Is Ugly Number
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Is Ugly Number: Is Ugly Number
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior. The description says only "Is Ugly Number: Is Ugly Number" and does not state what the tool returns, whether it accepts a number via some implicit context, what defines an ugly number, or any side effects. This is not transparency; it is a restatement with zero behavioral content.
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 text is short, but this is severe under-specification rather than conciseness. The repetition "Is Ugly Number: Is Ugly Number" wastes its only available sentence without conveying meaning. It does not front-load useful information because there is no useful information, and the symmetry with sibling tools would deserve more specificity.
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?
Despite zero parameters and no output schema, the description must still clarify what the tool checks and how to invoke it. With only a tautological phrase, the description is completely inadequate for an AI agent trying to decide whether to call x402-is-ugly-number versus dozens of nearby predicates. The huge sibling list highlights the need for differentiation, which is entirely absent.
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?
The input schema has zero parameters, so there are no parameter semantics to document. Baseline 4 is available for zero-parameter tools, but the description still leaves a mystery: if there are no parameters, how does the tool know which number to test? The schema coverage is technically 100%, but the tool's actual calling contract is unexplained, which prevents a higher score.
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 is a tautology: "Is Ugly Number: Is Ugly Number" merely restates the tool's name, which itself is a phrase-level predicate. It echoes the name/title without providing any explanation of what the tool does or what defines an ugly number. With no effective additional information, an agent cannot understand what the tool actually evaluates.
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 guidance about when to use this tool, what inputs are needed, or how it differs from the massive set of sibling predicates like x402-is-ugly or x402-is-prime. The context signals show zero parameters, so the agent receives no hint about how the target number is supplied or when to prefer this tool over alternatives. The lack of usage guidance leaves the agent to guess.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.