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minia2a-mcp

x402-abi-encode

ABI Encode: Encode function arguments into ABI calldata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typesNoTypes to process

TDQS

C2.9/5.0
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, but it only names the transformation. It does not specify the output format (e.g., hex-prefixed calldata), the expected input syntax for types, error behavior, or whether values must accompany the types. This is a thin description for an encoding tool.

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 with the operative verb 'Encode', containing no filler. The leading 'ABI Encode:' label is redundant with the tool name but does not detract meaningfully from readability.

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?

Although the tool is simple with one parameter, the absence of input format, output format, and a clear explanation of how 'types' maps to actual function arguments leaves the agent unable to reliably construct a correct invocation. No annotations or output schema compensate for these gaps.

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?

The schema has 100% description coverage for the single 'types' parameter, which sets a baseline of 3. The description adds that these types are used to encode function arguments into calldata, but it still does not explain how types should be formatted or where argument values are supplied. The parameter remains vague despite the textual additions.

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 specific operation (encode) and resource (function arguments into ABI calldata), which is more informative than the tool name alone. It is naturally distinguished from the sibling x402-abi-decode by the direction of the operation. However, the relationship between 'function arguments' and the lone 'types' parameter is imprecise, leaving ambiguity about what the input actually contains.

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 about when to use this tool versus alternatives like x402-abi-decode or x402-abi-lookup. No context, exclusions, or conditions are provided, so the agent must infer usage solely from the name and generic description.

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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