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minia2a-mcp

x402-remove-whitespace

Remove Whitespace: Remove Whitespace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

D1.8/5.0
Behavior1/5

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 and delivers nothing. It does not state what input the tool operates on, which characters count as whitespace, or whether the transformation is lossy. This is a complete absence of behavioral context for a tool that performs a destructive text mutation.

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 extremely short, but its brevity stems from tautology rather than compression — 'Remove Whitespace' merely echoes the name. Nothing is front-loaded because no information exists to front-load. Under-specification does not qualify as conciseness.

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?

The tool has no annotations, no output schema, an empty input schema, and a tautological description, leaving the agent without an invocation model or a behavioral contract. The large sibling set creates genuine selection ambiguity with collapse-spaces, normalize-whitespace, and trim-lines, none of which this description resolves. This fails even the minimum-viable bar for a text-processing utility.

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

Parameters4/5

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

The input schema is empty (0 parameters), so there are no parameter semantics for the description to clarify; per the rubric this warrants a baseline of 4. The oddity that a text-processing tool declares zero parameters is real, but it is a behavioral/usage gap rather than a parameter-documentation gap, so it is accounted for in other dimensions.

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 reads 'Remove Whitespace: Remove Whitespace' — a pure tautology that restates the tool name with zero additional specification. It does not clarify whether the tool strips leading/trailing whitespace, removes all whitespace characters, or collapses runs of spaces, and it fails to distinguish the tool from near-identical siblings like x402-collapse-spaces and x402-normalize-whitespace.

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?

No usage guidance appears anywhere in the description — there is no when-to-use statement, no conditions, and no mention of alternatives. The sibling list contains several overlapping text utilities (collapse-spaces, normalize-whitespace, trim-lines, remove-empty-lines), so an agent has nothing to help it select among them. The absence is a gap rather than a misdirection, hence a 2 rather than a 1.

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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