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minia2a-mcp

x402-ai-social-bio

AI Social Bio: Generate a social bio with AI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aboutNoAbout to process
platformNoPlatform to process

TDQS

C2.8/5.0
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 disclosure burden, yet it only restates the tool's purpose. It does not disclose output format, bio length, tone behavior, how 'platform' affects the result, or any side effects. The description adds no behavioral context beyond what the name already conveys.

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

Conciseness3/5

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

The description is short, but it spends its limited words redundantly: the 'AI Social Bio' prefix mirrors the tool name and 'with AI' repeats the 'ai' already in the name. The single sentence carries almost no information beyond the name itself, so brevity is achieved at the expense of substance.

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

Completeness2/5

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

For a 2-parameter tool with no annotations and no output schema, this description is incomplete: it omits parameter semantics, expected output shape (single bio vs. multiple options), and platform constraints. The schema's own descriptions are equally vacuous, so an agent has essentially no information to call the tool correctly beyond guessing.

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 description coverage is 100%, so the baseline is 3; both 'about' and 'platform' have schema entries. However, those descriptions ('About to process', 'Platform to process') are tautological, and the tool description itself adds no parameter meaning, leaving the agent guessing what content 'about' should hold and what value range 'platform' accepts.

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 states a clear verb and resource: 'Generate a social bio.' This is specific enough for an agent to understand the core function. However, it does not differentiate from generative siblings like x402-ai-tweet, x402-ai-tagline, or x402-ai-headline, and the trailing 'with AI' just repeats what the tool name already implies.

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 the many alternative content-generation siblings, no mention of what 'about' should contain, and no examples of valid 'platform' values. An agent must infer usage entirely from the tool name and schema.

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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