x402-language-detect
x402-language-detect: Detect text language. 5 free trial calls per registered wallet.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Input to process |
x402-language-detect: Detect text language. 5 free trial calls per registered wallet.
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | Input 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. It adds the trial-call limit context, which is useful, but it does not disclose output format (e.g., language code vs. name), error behavior, or requirements beyond wallet registration. An agent cannot predict what a successful response looks like or what failures may occur.
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 short and front-loaded with the core action, followed by the trial-call note. Both sentences carry useful information with no filler. The only minor waste is the redundant 'x402-language-detect:' prefix that repeats the tool name.
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 no output schema, the description should explain expected return values, but it does not. An agent knows the tool detects language and that there is a trial limit, but not what the output looks like or how to handle the input. For a simple tool this is a moderate gap, but the lack of return-format information and sibling differentiation makes it incomplete.
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% (the single 'input' parameter has a description), so the baseline is 3. The tool description implies 'input' is the text to detect, adding a small amount of meaning beyond the generic 'Input to process.' However, it does not specify expected format, language scope, or length limits, leaving the semantics somewhat underdefined.
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 specific verb and resource: 'Detect text language.' This is clear and unambiguous about the tool's core purpose. However, it does not differentiate from sibling tools like x402-language-guess or x402-language-name, which likely perform similar or overlapping functions.
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 about when to use this tool versus alternatives such as x402-language-guess or x402-ai-translate. The mention of '5 free trial calls per registered wallet' is a usage constraint (billing/rate limit), not guidance on selection. No preconditions, exclusions, or alternative routing are provided.
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.