x402-time
Time: UTC time, unix, ms. ๐ 5 free trial calls per registered wallet
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Time: UTC time, unix, ms. ๐ 5 free trial calls per registered wallet
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full behavioral disclosure. It does state the returned data types and the free trial limit, which is useful. However, it doesn't describe the output structure, format details (e.g., ISO 8601 vs integer), or any wallet/registration requirements beyond the trial note.
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 extremely concise: one sentence states the tool's output and another covers the trial limitation. Every element earns its place, and the core information is front-loaded.
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 zero-parameter read-only utility, the description is largely complete: it names all three output values and notes a key usage constraint. The lack of a formal output schema is partially offset by naming the fields, though exact formatting and the response container are not specified.
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 tool has zero parameters, which sets the baseline at 4. The description needs to add no parameter context, and it doesn't attempt to. The empty schema is self-explanatory.
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 identifies the tool as providing current time data with specific fields: UTC time, unix, and milliseconds. While it lacks an explicit verb like 'get' or 'returns', the listed outputs clearly convey what the tool does and distinguish it from conversion or date-math siblings.
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?
Usage is implied: an agent can infer this tool is for obtaining the current time in UTC, unix, or milliseconds. However, it does not explicitly state when not to use it or name alternative tools such as x402-date, x402-timestamp-pretty, or x402-time-utilities. The trial-call note adds a practical constraint but not comparative 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.