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NeuralVerge Business Intelligence

Rakuten shop

run_rakuten_shops

Get one Rakuten Ichiba shop storefront by shop_code or URL: name, description, logo, cover, rating and review count. Flat 5 points per successful run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shopYesShop code or URL. e.g. sawaicoffee-tea

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses the cost model ('Flat 5 points per successful run') and the returned fields, but says nothing about authentication needs, not-found behavior, rate limits, or whether a failed lookup still incurs cost.

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?

Two sentences, no waste, with the resource scope and lookup key front-loaded and cost disclosure trailing. Efficient and easy to scan.

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?

With no output schema, the enumerated return fields are genuinely helpful, and the cost note is a plus. However, it omits what happens on an invalid or missing shop, and does not clarify the relationship to run_rakuten_shops_items for retrieving the shop's inventory.

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% and the single 'shop' parameter is already documented with type and an example ('sawaicoffee-tea'). The description restates 'by shop_code or URL' without adding format, canonicalization, or fallback behavior, so it adds little beyond the schema (baseline 3).

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

Purpose5/5

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

States a specific verb ('Get'), resource ('one Rakuten Ichiba shop storefront'), lookup key ('by shop_code or URL'), and enumerates the returned fields: name, description, logo, cover, rating and review count. This clearly separates it from run_rakuten_shops_items, which returns a shop's items rather than the storefront record.

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?

Usage is only implied: an agent infers it should call this when it has a shop code or URL. There is no explicit when-to-use/when-not guidance and no mention of the closest sibling run_rakuten_shops_items, so routing between storefront lookup and item listing is left to 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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