x402-matrix-is-square
Matrix Is Square: Matrix Is Square
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Matrix Is Square: Matrix Is Square
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of disclosing behavior. It discloses nothing: no mention of what input is expected, no mention of a boolean result, no mention of errors for non-square matrices, and no side effects or return format. The tool's behavior is entirely 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?
The description is extremely short but this is under-specification, not effective conciseness. Repeating the same phrase twice adds no useful structure or front-loaded information and wastes the only opportunity to clarify the tool.
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?
Even though there are no parameters and no output schema, the description still fails to explain what the tool computes, what it returns, or how an agent should use its result. The zero-parameter schema reduces the required context, but the description remains too empty to be considered complete.
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 input schema is empty and schema description coverage is 100%, so there are no parameters to document. The description adds nothing, but the zero-parameter schema itself carries the parameter burden, making the baseline 4 appropriate.
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 is a pure tautology: 'Matrix Is Square: Matrix Is Square' restates the tool name without adding a verb, resource explanation, or expected result. It does not differentiate this tool from the many sibling matrix-* and is-* tools, so an agent cannot tell whether this is a check, transformation, or query.
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 usage guidance is provided. The description does not say when to use this tool instead of related tools like x402-matrix-shape, x402-matrix-rows, x402-is-square-free, or other matrix utilities. An agent has no basis for selecting it over alternatives.
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.