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minia2a-mcp

x402-qr-generate

QR Generate: QR Generate

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoData to process
sizeNoSize to process
inputNoInput to process

TDQS

D1.3/5.0
Behavior1/5

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, and it says nothing. There is no mention of what output is returned (image? URL? base64?), whether anything is persisted, whether size limits apply, or how data is processed. The description reveals zero behavioral traits.

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 short, but this is under-specification, not conciseness. A single tautological sentence earns no structural credit; there is no front-loaded key information because no information is present 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?

The tool has 3 vaguely described parameters, no annotations, no output schema, and sits among thousands of similar tools including x402-qr. The description is entirely inadequate for an agent to invoke it correctly, leaving every aspect — input format, size semantics, output, and differentiation from siblings — unknown.

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?

While schema coverage is nominally 100%, all three parameter descriptions are generic placeholders ('Data to process', 'Size to process', 'Input to process') that would fit any tool. The description text adds nothing to clarify the meaning of 'data' vs 'input', what units 'size' uses, or which fields are required. The empty placeholder-level schema descriptions do not justify a baseline of 3.

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 'QR Generate: QR Generate' is a pure tautology that merely restates the tool name. It contains no verb, no resource, and no detail about what the tool actually does (e.g., generates a QR code image from input data). It fails to distinguish itself from sibling tools like x402-qr, x402-barcode-generate, or x402-avatar-generate.

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?

There is zero guidance on when to use this tool or when to prefer an alternative. Among thousands of siblings including x402-qr and x402-barcode-generate, the description gives no selection criteria, no prerequisites, and no exclusions. An agent has no basis to choose this over related generation tools.

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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