x402-lorem-ipsum
Lorem Ipsum: Lorem Ipsum
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Count to process | |
| words | No | Words to process |
Lorem Ipsum: Lorem Ipsum
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Count to process | |
| words | No | Words to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description is silent on side effects, return format, or any behavioral characteristics. An agent cannot infer whether this is a read-only generator, a mutator, or a network-dependent tool.
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 only four words long, but this is under-specification rather than conciseness. There is no front-loaded useful information, no structured explanation, and every word is wasted.
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?
A text-generation tool with no annotations, no output schema, and a placeholder description is completely inadequate. An agent cannot know what the output will be, how the two parameters interact, or what edge cases exist. The sibling x402-lorem exists, and nothing here helps the agent choose between them.
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%, with both 'count' and 'words' having descriptions. The tool description itself adds no parameter meaning, but per baseline for high schema coverage, a score of 3 is appropriate even though the schema descriptions are quite vague.
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 is identical to the tool name: 'Lorem Ipsum: Lorem Ipsum'. It provides no verb, no resource, and no indication of what action the tool performs. It does not distinguish this tool from the closely related sibling x402-lorem.
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, when not to use it, or which alternative to choose. The description gives no context about whether this tool generates, processes, or validates lorem ipsum text, leaving an agent with no basis for selection.
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.