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minia2a-mcp

x402-tab-to-space

Tab To Space: Convert tab to space.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputNoInput to process
widthNoWidth to process
tabWidthNoTabWidth to process

TDQS

D1.9/5.0
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 states only the core transformation (tab to space) and reveals nothing about how tab width is applied, how mixed tab/space content is handled, whether the operation is lossy, or what the output format looks like.

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 the single sentence merely restates the name in prose and earns no informatinal weight. This is under-specification rather than efficient conciseness — a terse description that conveys nothing beyond the identifier is not a model of economy.

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 3 cryptically described string parameters, no annotations, no output schema, and a large family of related whitespace transformations, the description is wholly inadequate. An agent cannot correctly select this tool or construct a valid invocation for it, e.g., it cannot know what 'width' refers to or what conversion rule will be applied.

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 schema descriptions are vacuous boilerplate — "Input to process", "Width to process", "TabWidth to process" — that explain nothing about meaning, format, or defaults. The tool description adds zero parameter information. The string typing of width and tabWidth is unexplained, leaving an agent unable to determine valid values such as "4" versus "tab".

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

Purpose2/5

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

The description "Convert tab to space" is a near-verbatim restatement of the tool name "x402-tab-to-space" — it adds a generic verb but no new information about scope, target, or semantics. It also does nothing to distinguish this tool from closely related siblings like x402-space-to-tab, x402-indent, or x402-dedent. This is effectively a tautology.

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?

There is no guidance on when to use this tool versus alternatives. In particular, the inverse operation x402-space-to-tab and related whitespace-conversion tools exist in the sibling list, yet the description offers no selection criteria, prerequisites, or context for when tab-to-space conversion is appropriate.

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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