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minia2a-mcp

x402-ai-regex

AI Regex: Generate a regex from a natural-language description.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descNoDesc to process
descriptionNoDescription to process

TDQS

B3.4/5.0
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 core action and gives no detail about output format (e.g., returns a regex string), whether the regex is escaped or includes flags, any dependency on external AI services, or error behavior. There is minimal beyond what the name already implies.

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

Conciseness5/5

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

The description is a single, compact sentence that front-loads the purpose. There is no waste or redundant content; every word earns its place.

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 fairly simple tool, the description is still incomplete. It does not explain return value semantics, clarify the ambiguous dual parameters, or provide examples. With no output schema and no annotations, the agent is left uncertain about how the two optional 'desc'/'description' fields behave and what the tool actually returns.

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. The property descriptions ('Desc to process' and 'Description to process') are generic and redundant, and the tool description does not clarify whether both parameters are equivalent, which one takes precedence, or whether either is required. The description adds no value beyond the schema's minimal parameter documentation.

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

Purpose5/5

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

The description states a clear, specific action: 'Generate a regex from a natural-language description.' It names both the verb (generate) and the resource (regex), and the 'AI Regex' prefix makes the intent obvious. This distinguishes it from siblings like x402-regex-explain and x402-escape-regex.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: when you have a natural-language description and need a regex. However, it does not explicitly mention when to prefer this over alternatives (e.g., x402-regex-explain for explaining an existing regex) or provide any exclusion criteria. The guidance is present only by inference.

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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