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minia2a-mcp

x402-data-size-humanize

Data Size Humanize: Data Size Humanize

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bytesNoBytes to process
valueNoValue to process

TDQS

D1.4/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden of behavioral disclosure, and it discloses nothing. There is no mention of return format, unit conventions (binary vs decimal), handling of invalid input, or side effects. The description contains zero behavioral claims beyond repeating the tool's label.

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

Conciseness2/5

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

This is under-specification, not conciseness. The 'Title: Body' structure contains two identical phrases, so every word is wasted repetition. There is no front-loaded information because there is no information at all.

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

Completeness1/5

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

With no output schema and no annotations, the description is the sole source of contextual information, and it provides none. An agent cannot determine the expected input format (numeric string, '10MB', '1024'?), the output convention (B/KB/MB vs KiB/MiB), or how this tool differs from several siblings. The definition is completely inadequate for a 2-parameter tool.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3 even with no parameter info in the description. Both 'bytes' and 'value' are documented as strings, but their schema descriptions ('Bytes to process' / 'Value to process') are vague about expected format, and the tool has two optional parameters with no clarification of which to supply. The description adds nothing to resolve that ambiguity, so it stays at baseline.

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

Purpose1/5

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

The description, 'Data Size Humanize: Data Size Humanize,' is a verbatim restatement of the tool name — a pure tautology. It does not state what the tool does (e.g., convert a byte count to a human-readable size string), what the output looks like, or what units are involved. It is also indistinguishable from adjacent siblings such as x402-file-size-humanize, x402-format-bytes, x402-data-size, and x402-throughput-humanize.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus its siblings. There is no context, no exclusion criteria, and no mention of alternatives like x402-file-size-humanize or x402-data-size. An agent has no basis for selecting this tool over the many similar-looking size/format siblings.

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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