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minia2a-mcp

x402-random-joke

Random Joke: Generate a random joke.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoN to process
valueNoValue to process

TDQS

C2.6/5.0
Behavior2/5

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 only states that a joke is generated, offering no information about side effects, network behavior, whether output is deterministic, return format, or whether the optional parameters affect the result. For a tool with zero annotation coverage, this leaves the agent guessing about the tool's observable behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only five words, so it is undeniably short, but the leading 'Random Joke:' prefix merely restates the tool name and adds no value, leaving just one informative clause. It is more under-specied than appropriately concise; while there is no fluff, there is also almost no substance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with two confusingly-named parameters, no annotations, and no output schema, the description is not complete enough. It does not clarify parameter usage, expected output shape, or how this joke tool differs from the many joke/fact/quote siblings. An agent would struggle to invoke it with correct parameter values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the parameter descriptions are generic boilerplate ('N to process', 'Value to process') that carry no meaningful semantics for a joke generator. The tool description does not explain how 'n' or 'value' relate to joke generation (e.g., count, category, seed). The descriptions fail to compensate for the schema's templated, uninformative text, and the parameter names seem unrelated to the stated purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb+resource: 'Generate a random joke.' This is specific enough to convey the tool's basic function. However, it does not differentiate the tool from closely related siblings such as x402-dad-joke and x402-chuck-norris, nor does it clarify the scope ('random' from what source/category).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives. It does not mention that x402-dad-joke, x402-chuck-norris, x402-random-fact, or x402-random-quote exist for other entertainment outputs, and no context signals (e.g., prerequisites, input constraints) are provided. An agent has nothing to decide when this tool is the right choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

D1.6/5.0
Disambiguation1/5

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.

Naming Consistency2/5

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.

Tool Count1/5

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.

Completeness2/5

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.

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