x402-is-luhn
Is Luhn: Check whether a number passes the Luhn checksum (used for credit cards and IDs). Provide value.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Is Luhn: Check whether a number passes the Luhn checksum (used for credit cards and IDs). Provide value.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 explains the algorithm but does not disclose the return format (boolean vs structured result), handling of malformed input, or how the value is supplied given the schema's empty parameter set. For a pure predicate the safety profile is implied, but invocation behavior is still opaque.
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?
Two sentences with little waste, and the purpose is front-loaded. But the 'Is Luhn:' prefix redundantly echoes the tool name, and the cryptic 'Provide value' second sentence earns little keep given it contradicts the schema.
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 simple predicate with an empty input schema and no output schema, the description needed to explain how the value is passed and what the caller receives back; it does neither. The absence of any differentiation from the several credit-card sibling validators compounds the gap. An agent reading only this description and schema cannot reliably invoke the 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 baseline for 0 params is 4, and the description does introduce the concept of a 'value' that the empty schema lacks. However, 'Provide value' conflicts with the schema's zero-property signature - the agent is told to supply something the schema says cannot be supplied - and the type or format of that value is never clarified.
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?
States a specific verb ('Check'), algorithm ('Luhn checksum'), and domain context ('credit cards and IDs'), which distinguishes it from sibling validators like x402-credit-card-validate or x402-card-validator that test actual card details rather than the bare checksum. The trailing 'Provide value' instruction is confusing about how the input is supplied, but the core function is unambiguous.
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 or alternative routing guidance. With overlapping siblings such as x402-is-credit-card, x402-credit-card-validate, x402-validate-credit-card, x402-card-validator, an agent has no way to choose between this bare Luhn predicate and a full card validation tool. The 'credit cards and IDs' parenthetical hints at context but never states when Luhn alone is the appropriate call.
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.