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minia2a-mcp

x402-ai-audit

AI Contract Audit: Smart contract security audit, solidity/rust/move vulnerability scan. AI deep audit across chains — 3x sampled, deduplicated. Catches business-logic, economic, cross-contract flaws (uninitialized owner, account confusion, missing signer auth). LIMITATIONS: AI audit is probabilistic — may miss vulnerabilities or report non-issues; treat as guidance, not proof; critical findings require manual verification. Case: minia2a.uk/blog/blog-ai-contract-audit-august-2026.html

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoCode to process
langNoLang to process
sourceNoSource to process
contractNoContract to process
languageNoLanguage to process
source_b64NoSource_b64 to process

TDQS

B3.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations to describe side effects or risk, the description carries that burden and does it well: it explicitly states the audit is probabilistic, may miss vulnerabilities or report non-issues, and is guidance not proof. It also discloses the sampling/deduplication approach and examples of flaw detection, providing useful behavioral context.

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 first two sentences are informative and front-loaded, and the LIMITATIONS section is useful. But the trailing 'Case:' URL adds no invocation value and could be removed; the description is moderate-length with some noise.

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?

With no output schema, no annotations, and six optional parameters, the agent still doesn't know the expected input format or which parameter to populate. The description explains what the tool does but not how to call it, leaving a significant gap for a tool with this many parameters and several neighboring audit tools.

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 per the rubric. However, the schema descriptions are tautological ('Code to process', 'Lang to process'), and the description adds no clarity about which of the six overlapping parameters to provide or how language/source/source_b64 relate. It does not exceed the baseline.

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 clearly states the tool performs smart contract security audits across solidity/rust/move, with a specific verb and resource. However, it does not distinguish itself from the closely named sibling x402-smart-contract-audit, so it falls short of full differentiation.

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 opening establishes a clear use case — smart contract security audit — which implies when to use it. But it never addresses when not to use it or names alternatives like x402-smart-contract-audit or x402-contract-scan, leaving selection to the agent's 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.

Resources