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minia2a-mcp

x402-abi-decode

ABI Decode: Decode ABI-encoded calldata into human-readable arguments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoData to process
inputNoInput to process
typesNoTypes to process

TDQS

C2.7/5.0
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 conveys the core read-like decode behavior and the 'human-readable' output format, but it does not disclose whether the 'types' parameter is required for decoding, whether the function selector is parsed, how errors are surfaced, or any edge cases. An agent cannot predict the tool's operation beyond the most basic statement.

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 a single front-loaded sentence with zero filler, and the 'ABI Decode:' label makes the domain immediately obvious. The only minor flaw is a slight redundancy between the label and the verb 'Decode' in the sentence itself. Brevity here is achieved at the cost of substance, but structurally it is clean.

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?

ABI decoding is a domain-specific operation with nontrivial input requirements, yet there is no output schema and no annotation coverage to compensate. The description does not explain the parameter roles, the expected types format, whether types are optional, or the shape of the decoded result. For an agent to call this correctly, too much is left to guesswork.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema description coverage is nominally 100%, every parameter description is a vacuous placeholder ('Data to process', 'Input to process', 'Types to process'). The two parameters 'data' and 'input' appear to serve the same purpose but the description does not explain the difference or which one carries the calldata, nor does it specify the syntax expected for 'types'. The tool description adds no clarifying information about any parameter.

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 verb ('Decode'), a specific resource ('ABI-encoded calldata'), and the outcome ('human-readable arguments'). This is clear and concrete. However, it does not explicitly differentiate from closely related siblings like x402-abi-encode, x402-event-decode, x402-tx-decode, or x402-abi-lookup, leaving the agent to infer the boundaries.

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 the many decode/ABI-related siblings. The description merely states what the tool does; it provides no exclusions, no alternatives, and no context such as 'use when you have raw calldata hex from a transaction.' An agent selecting among x402-abi-encode, x402-event-decode, x402-tx-decode, and this tool gets no routing help.

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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