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FishClaw MCP

FishClaw MCP

by TnoobT
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

generate_product_description

Read-only

Generates Xianyu product descriptions for technical topics, structured as self-introduction, service category, and tech keywords.

Instructions

根据技术主题生成闲鱼商品描述文案（自我介绍 + 服务分类 + 技术关键词三段式）。 生成结果可直接传入 draft_item 的 description 参数。

Args: topic: 技术主题或服务内容，如"AIGC绘画"、"Python爬虫"等。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "generate_product_description"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

The annotation readOnlyHint=true already indicates no side effects. The description adds that the tool generates text output but does not disclose behavioral traits like error handling, rate limits, or output format beyond the mention of 'three-part format'. It does not contradict annotations.

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?

The description is concise: two sentences plus an Args line. It front-loads the purpose and usage. However, it lacks headings or formatted separation, and is provided only in Chinese. Still, no extraneous content.

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

Completeness4/5

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

Given the tool's simplicity (single parameter, output schema exists), the description covers the input meaning and intended next step (draft_item). It does not explain edge cases or full output behavior, but with the output schema available, this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description provides a meaningful explanation for the 'topic' parameter: it specifies it as a 'technical topic or service content' and gives examples like 'AIGC drawing' and 'Python crawler'. This compensates for the schema gap and adds practical guidance.

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 that the tool generates a Xianyu product description based on a technical topic, and specifies the three-part format (self-introduction, service category, technical keywords). It also indicates the output can be used directly in draft_item's description parameter. While it doesn't explicitly distinguish from sibling tools, the purpose is specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage before draft_item but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of prerequisites, when-not to use, or comparison with siblings like get_selling_items or manage_item. Users must infer the appropriate context.

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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