x402-reading-time
Reading Time: Estimate reading time for text.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Reading Time: Estimate reading time for text.
| 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 behavioral burden. It reveals only the operation and omits key behavioral details: the assumed reading-speed model, the output unit (minutes vs. seconds vs. human-readable), and how text input is supplied given the empty schema.
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?
One short sentence that is efficient and front-loaded with the core operation. The 'Reading Time:' prefix redundantly restates the tool name and does not earn its place, but overall the description is appropriately sized for a tool this simple.
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 an empty input schema and no output schema, the description does not explain how the caller provides the text or what the returned reading time looks like. An agent cannot determine the invocation contract beyond 'text is involved,' which is a significant gap even for a simple tool.
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 schema has zero properties, so there are no parameters to document and the baseline of 4 applies. The phrase 'for text' loosely signals the input domain, which is all the description could add given there are no parameters.
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 clear verb ('Estimate') and resource ('reading time for text'), which is more than a tautology of the tool name. However, it does not differentiate from text-analysis siblings like x402-reading-level, x402-readability-score, or x402-word-count, leaving an agent to rely on names alone for distinction.
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 when-to-use, when-not-to-use, or alternative-tool guidance is provided. There is no hint about when an agent should prefer this tool over related text-metrics siblings, so usage context must be inferred purely from the tool name.
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.