x402-format-scientific
Format Scientific: Format a number in scientific notation.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Format Scientific: Format a number in scientific notation.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It fails to disclose the critical behavioral gap: the input schema is empty, so it is unexplained how the 'number' to be formatted is supplied to the tool. There is also no mention of default precision, digit handling, exponent formatting details, or what the returned string looks like.
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 easy to scan, but the first fragment 'Format Scientific:' merely restates the tool name and adds no information. The second sentence is the only substantive content. This is efficient in length but mildly redundant and under-specified rather than earnestly concise.
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?
With no parameters, no annotations, and no output schema, the description is the only source of invocation knowledge — and it tells the agent nothing about how to provide the input number. An agent reading the schema sees zero properties and the description offers no resolution, making a correct call effectively impossible without external knowledge.
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 per the rubric. The description adds minimal semantic context by naming the operand ('a number'), but since the schema has no properties, there is nothing further to document. The description does not conflict with the schema.
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 (Format), a resource (a number), and the target representation (scientific notation), so an agent knows what the tool does. However, it does not differentiate from the near-identical sibling x402-scientific-notation, which plausibly does the same thing — the description gives no basis for choosing one over the other.
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 like x402-scientific-notation, x402-engineering-notation, x402-format-significant, or x402-number-humanize. The only thing an agent can infer is 'use when you need scientific notation,' which is circular with the purpose rather than actionable routing 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.