x402-guess-number
x402-guess-number: Bulls-and-cows solver from guess history
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| guesses | No | Guesses to process | |
| history | No | History to process |
x402-guess-number: Bulls-and-cows solver from guess history
| Name | Required | Description | Default |
|---|---|---|---|
| guesses | No | Guesses to process | |
| history | No | History 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. It only restates the tool's core function and gives no insight into behavior such as whether it returns the next guess, validates input, or which parameter drives the solving. The description is essentially a label rather than a behavioral disclosure.
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 a single, efficient sentence with no wasted words. The leading 'x402-guess-number:' prefix is redundant since the tool name is already provided, but it does not meaningfully harm clarity.
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 two ambiguous string parameters, no annotations, and no output schema, the description leaves critical gaps: how guesses and history should be encoded, the length or digit range of the secret, how feedback is represented, and what the tool returns. An agent cannot reliably invoke this tool correctly from the provided context alone.
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 descriptions ('Guesses to process' and 'History to process') just restate the parameter names and add no real semantics. The tool description adds that history is the basis for solving, but it does not clarify the relationship between guesses and history or the expected format of either string.
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 function ('Bulls-and-cows solver') and the resource it operates on ('guess history'), making the purpose clear. It doesn't explicitly contrast with similar game-solver siblings like x402-mastermind or x402-wordle-solve, but the resource and game are specific enough to identify the tool.
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?
The phrase 'from guess history' clearly implies when to use this tool: when the agent has a history of Bulls-and-Cows guesses to solve from. The use case is clear, but there is no explicit exclusion guidance naming alternative tools or conditions that would make a different solver more appropriate.
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.