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minia2a-mcp

x402-ai-blog-outline

AI Blog Outline: Generate a blog post outline with an AI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoTitle to process
topicNoTopic to process

TDQS

B3.1/5.0
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 mentions 'with an AI,' which hints at an AI-backed generation process, but it does not describe the outline format, determinism, expected length, or any side effects. The description is too thin to inform an agent about the tool's runtime behavior.

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

Conciseness4/5

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

The description is short and front-loaded, wasting no words. The prefix 'AI Blog Outline:' is redundant with the tool name and 'with an AI' repeats the AI concept, but the overall length is appropriate and easily scannable.

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 two-parameter AI generation tool with no output schema, this definition lacks essential context. It does not explain what an agent should do if both parameters are omitted, nor what shape the returned outline will take. Sibling tools like x402-ai-outline and x402-ai-headline are not differentiated by usage cues, making selection harder.

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 coverage is 100%, so the baseline is 3; both 'title' and 'topic' have descriptions. However, those descriptions are tautological ('Title to process' and 'Topic to process') and provide no additional meaning. The tool description also does not clarify how the two parameters interact or whether one is preferred over the other.

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 clearly states a specific verb and resource: 'Generate a blog post outline with an AI.' The phrase 'blog post' distinguishes this from the generic x402-ai-outline and other sibling tools. Despite the redundant prefix, the core statement is specific and unambiguous.

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 such as x402-ai-outline or other content-generation tools. It does not state whether both title and topic are required, nor which parameter takes priority. This lack of when/when-not guidance leaves the agent to infer usage from the name alone.

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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