x402-rect-prism-surface
Rect Prism Surface: Surface area of rect prism.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Rect Prism Surface: Surface area of rect prism.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only restates the core calculation. It does not mention input requirements, assumptions about units, edge cases, return format, or the fact that the schema exposes zero parameters, leaving agent expectations vague.
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 appropriately terse and front-loaded: 'Rect Prism Surface: Surface area of rect prism.' However, the first phrase largely duplicates the tool name, and the second is only a slight restatement. It is concise but not maximally informative.
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 computation tool with zero parameters and no output schema, the description should clarify how the tool receives inputs and what it returns. It does neither. Among hundreds of sibling tools, it also fails to differentiate itself beyond the surface/volume distinction already present in names.
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 no properties, so the baseline is 4 per the rubric. The description adds no parameter-level detail because none is needed. Still, it does not explain how the tool obtains the prism dimensions, which is a subtle gap but outside strict parameter semantics.
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 tool's function as computing the surface area of a rectangular prism, which is a specific resource and operation. It also implicitly distinguishes it from the sibling x402-rect-prism-volume. However, it lacks a verb and relies on the noun phrase 'Surface area,' making it slightly less explicit than the ideal.
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 on when to use this tool versus alternatives such as other shape-surface calculators or the volume tool. It does not state prerequisites, context, or scenarios where this tool is the appropriate choice.
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.