x402-calories-to-joules
Calories To Joules: Convert calories to joules.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Calories To Joules: Convert calories to joules.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden but only names the conversion. It fails to address the classic calorie ambiguity (small/thermochemical calorie ≈ 4.184 J vs. large/food Calorie = 4184 J) and does not explain how input is supplied given the input schema is empty. An agent cannot anticipate output format, precision, or unit definition.
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 and front-loaded, which is good, but the leading 'Calories To Joules:' fragment merely restates the tool name and does not earn its place. The body 'Convert calories to joules' adds only marginal information beyond the name. This is under-specification rather than efficient conciseness.
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 tool with no annotations, no output schema, and an empty input schema, the description is far from complete. An agent cannot determine the input mechanism, which calorie definition is used, or what the return value looks like. The sibling name says more than the description does.
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?
With 0 parameters, the baseline is 4, but the description adds no meaning about how a conversion value is actually passed to the tool — an oddity since a conversion tool normally requires an input. The empty schema leaves this entirely unexplained, and the description does nothing to compensate. A 3 reflects the unexplained input mechanism.
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 verb and resource — 'Convert calories to joules' — making the operation and direction unambiguous. This implicitly differentiates it from the inverse sibling x402-joules-to-calories. However, it largely restates the tool's own name, 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?
There is no guidance on when to use this tool versus the many sibling conversion tools, such as x402-joules-to-calories for the reverse direction or x402-units-convert for general conversion. No context, prerequisites, or exclusions are provided. The description is purely a definition, not usage guidance.
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.