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minia2a-mcp

x402-cups-to-ml

Cups To Ml: Convert cups to ml.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

C2.5/5.0
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 does not state input format, output format, rounding behavior, whether it accepts fractional amounts, or whether it is a read-only calculation. A conversion tool is likely safe and pure, but the description doesn't disclose any of that explicitly.

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 and front-loaded, but it is minimal to the point of being terse. 'Cups To Ml: Convert cups to ml.' is repetitive: the title-like prefix restates the name and adds no additional value. It earns credit for brevity but loses points for tautological redundancy in the first part.

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?

Given a conversion tool with no annotations, no output schema, and no parameter schema, the description should explain how input is supplied, what precision or rounding is applied, and what the output looks like. None of that is present. It is incomplete for an agent to invoke correctly, especially since the schema defines no parameters at all.

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%, but only because there are zero parameters defined. The description mentions 'Cups' as the conceptual input but doesn't explain how to provide it. With 0 params in the schema, the description should clarify how the agent should pass the value, but it doesn't. Baseline 4 for 0 params is reduced because the tool presumably needs an input yet none is described.

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

Purpose3/5

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

The description states a specific verb and resource ('Convert cups to ml'), which is clear enough to understand the unit conversion intent. However, it doesn't distinguish itself from other unit-conversion siblings like ml-to-cups, tablespoons-to-ml, or teaspoons-to-ml, beyond the inherent name. The purpose is understandable but basic.

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, no mention of volume conversion context, or any limitation such as whether it handles fractional inputs, US vs imperial cups, or how the result is returned. The agent gets no help deciding whether this is the right tool among hundreds of conversion siblings.

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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