Amazon売上ダッシュボード
amazon-sales-dashboardASIN別売上/利益/在庫をlocalStorageで管理。月別推移グラフ (Browser-based tool)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
amazon-sales-dashboardASIN別売上/利益/在庫をlocalStorageで管理。月別推移グラフ (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?
With no annotations, the description carries the full burden and does disclose that data is stored in localStorage and the tool is browser-based, indicating client-side persistence. However, it omits important behavioral details such as data lifecycle, input methods, storage limitations, or what happens if localStorage is cleared.
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 extremely concise, consisting of two short clauses that convey the core functionality and storage mechanism without unnecessary words. Information is front-loaded and highly scannable.
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?
Given that there is no input schema, output schema, or annotations, the description provides adequate high-level context: ASIN-level metrics, monthly trend graph, and localStorage persistence. It could mention how data is entered or exported, but this is not essential for basic invocation of a dashboard tool with no parameters.
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 tool accepts zero parameters, so the description appropriately focuses on the tool's functionality rather than parameter syntax. Baseline of 4 applies because there are no parameter semantics to explain.
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 manages ASIN-specific sales, profit, and inventory data using localStorage, and displays monthly trend graphs. This specific verb+resource combination distinguishes it from the many Amazon-related calculators in the sibling list.
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 description implies client-side usage via 'localStorage' and 'Browser-based tool,' but does not explicitly state when to choose this over alternatives like affiliate-revenue-dashboard or other Amazon calculators. No exclusions or alternative recommendations are provided.
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.