x402-reading-level
Reading Level: Reading Level
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Input to process |
Reading Level: Reading Level
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Input to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, and it provides zero behavioral information. The agent learns nothing about what the tool returns, what scale the reading level is measured on, how input should be structured, or any edge-case behavior. This is indistinguishable from no description at all.
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 definition is short but this is under-specification, not conciseness. The repeated phrase "Reading Level: Reading Level" occupies the entire description without earning its place — no information is delivered to the agent.
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 one parameter, no output schema, and no annotations, the description is completely inadequate. The agent cannot determine the input contract, the output format, which reading-level algorithm is applied, or how this tool relates to the dozens of sibling text-analysis tools. Every dimension of needed context is missing.
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?
Although schema description coverage is formally 100%, the only parameter description is the boilerplate "Input to process," which conveys no meaning about input format, length expectations, or accepted content. The tool description itself adds nothing about the parameter. Because the schema text is vacuous despite its nominal coverage, the description fails to compensate, so the baseline of 3 is not earned.
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 "Reading Level: Reading Level" is a pure tautology that merely restates the tool name. It does not state a specific verb, what resource is operated on, which reading-level metric is used (e.g., Flesch-Kincaid, SMOG, grade level), or how this tool differs from siblings like x402-readability-score or x402-reading-time.
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 whatsoever on when to use this tool versus the many similar siblings such as x402-readability-score, x402-reading-time, x402-avg-sentence-length, or x402-avg-word-length. No context, exclusions, or alternative routing is mentioned.
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.