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minia2a-mcp

x402-format-significant

Format Significant: Format a number with significant figures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the operation ('format a number with significant figures') without revealing how many significant figures are applied, what rounding rules are used, whether the tool accepts arbitrary input, or what the return value looks like. The empty input schema is also not addressed, so an agent cannot infer how a number is supplied.

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?

The description is a single sentence, which is concise, but it is under-specified rather than efficiently structured. It contains a redundant label ('Format Significant') followed by a definition that mostly restates the tool name. There is no additional context or structure that helps an agent use the tool.

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?

Given the tool has no annotations, no output schema, and an empty input schema, the description is the only source of information. It is far from complete: it does not explain how to invoke the tool, how the number is supplied, what significant figures means in terms of output, or how it differs from similar siblings. An agent cannot correctly call this tool based on the provided definition.

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?

The input schema has zero properties, so the baseline is 4 for parameter semantics, but the description claims to format 'a number' without explaining how that number is passed. This is a critical gap: the description implies input but provides no parameter names, types, or formats, leaving an agent unable to construct a valid call. The description does not compensate for the empty schema.

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 clearly states a specific action: 'Format a number with significant figures.' This is a concrete verb+resource pair that tells an agent what the tool does. However, it does not distinguish this tool from closely related siblings like x402-sig-figs, x402-significant-digits, or x402-format-scientific, so it falls short of full sibling differentiation.

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 provides no guidance on when to use this tool versus alternatives. Given the huge sibling list includes x402-sig-figs, x402-significant-digits, x402-round-to, and x402-format-scientific, an agent has no basis for selecting this one. There is no mention of input expectations, context, or exclusions.

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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