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ANIME INTELLIGENCE / AI PURCHASE INTELLIGENCE

Universal Multilingual Purchase Decision

universal_purchase_decision
Read-onlyIdempotent

PAID. Convert a global natural-language buying request into an evidence-based purchase decision. Supports exact industrial parts through PARTS + MERCHANT INTELLIGENCE, anime collectibles through full shopping intelligence, and conservative candidate search for other commerce domains. Price: 0.10 USDC via x402.

Input Schema

NameRequiredDescriptionDefault
queryNo
domainNo
productNo
languageNo
quantityNo
destinationNo
requirementsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description adds meaningful context beyond them: the tool is paid at 0.10 USDC via x402. Knowing there is a per-call charge materially affects invocation decisions. It also discloses output character ('evidence-based', 'conservative candidate search'), though it does not describe failure or refusal behavior when evidence is thin.

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?

Three tight sentences with the paid signal front-loaded and domain coverage enumerated compactly. Every sentence carries information; no filler or redundant restatement of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values needn't be explained, and payment plus safety context is present. However, for a seven-parameter tool with nested objects and zero schema documentation, an agent still cannot assemble a correct call from the description alone, and routing against 24 siblings is unaddressed.

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?

Schema description coverage is 0% across seven parameters including two nested objects, so the burden falls entirely on the description, and the description adds nothing about query vs. product, the anyOf requirement, language, quantity, destination, or requirements. The only parameter-adjacent hint is the general multilingual/global framing.

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?

States a specific verb and resource: converting a natural-language buying request into an evidence-based purchase decision, and scopes coverage across industrial parts, anime collectibles, and other commerce domains. It is distinguishable from narrower siblings like parts_search or anime_market as the umbrella tool, though it never names which sibling handles which case explicitly.

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?

Domain coverage implies when to reach for this tool (broad/unknown commerce domains, exact parts, anime collectibles), and 'conservative candidate search for other commerce domains' hints at fallback behavior. But with 24 siblings including parts_intelligence, shopping_intelligence, and commerce_search, the description never states whether this tool supersedes them or when to prefer one over the other.

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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