商品説明文ジェネレーター
product-description-generator商品スペック入力→プラットフォーム別の出品用説明文を即生成 (Browser-based tool)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
product-description-generator商品スペック入力→プラットフォーム別の出品用説明文を即生成 (Browser-based tool)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context: it is browser-based, generates instantly ('即生成'), and follows an input-to-output flow. However, it does not disclose output format, possible side effects, or limitations, leaving behavior somewhat underspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence that front-loads the workflow and includes a useful parenthetical about being browser-based. It is efficient and free of fluff, though slightly less structured than ideal for a tool with no schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a browser-based tool with no structured schema or output schema, the description covers the core transformation and environment. It does not mention which platforms are supported or how this tool relates to sibling listing-template generators, so it is only minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameters, so per the rubric the baseline is 4. The description adds conceptual input semantics ('商品スペック入力') but no parameter-level details are needed because there are no structured parameters to document.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates platform-specific product listing descriptions from product specs, using a specific verb ('生成') and a clear input-output relationship. It does not explicitly distinguish itself from sibling tools like ec-template-generator or mercari-listing-template, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The arrow notation implies a usage context: provide product specs and receive platform-specific listing descriptions. It names no alternatives or exclusions, but the 'product spec input' condition gives minimal guidance on when to use it. This is more than no guidance, but it lacks explicit when-to-use or when-not-to-use direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.
Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.
202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.
The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.