x402-wiki-random
Wiki Random: Wiki Random
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | N to process | |
| value | No | Value to process |
Wiki Random: Wiki Random
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | N to process | |
| value | No | Value to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no descriptive content, the description reveals nothing about side effects, data requirements, response characteristics, or operational behavior. 'Wiki Random' could imply fetching a random Wikipedia article, but the description does not state or imply any concrete behavior.
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 short, but this is under-specification rather than concise clarity. A two-word tautology does not earn conciseness credit because it provides no usable information.
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?
Given the ambiguous name, two unexplained parameters, no annotations, and no output schema, the description is wholly inadequate. An agent cannot determine what inputs to provide, what the output will be, or how this tool differs from the many random-related siblings.
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?
Schema description coverage is 100%, but the parameter descriptions 'N to process' and 'Value to process' are generic placeholders that add no real meaning. The tool description provides no clarification on what 'N' or 'Value' mean in the context of a wiki-random operation, so the agent cannot construct a correct invocation.
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 'Wiki Random: Wiki Random' is a pure tautology of the tool name. It contains no verb, no resource, and no action, so an agent cannot tell what this tool does, especially when there are many sibling tools like x402-random, x402-random-word, and x402-random-fact nearby.
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?
No usage context, prerequisites, or alternative tool routing is provided. The description never explains when to choose this tool over the hundreds of available siblings, leaving the agent completely without 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.