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dsh-vision-dashscope

dsh-vision-dashscope

by ywleeo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_image

Generate an image from a text prompt using DashScope, save the result locally, and return its file path for preview.

Instructions

文生图。生成结果下载到本地输出目录并返回路径（配合 dsh-image-preview 可内联预览）。

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": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
sizeNo尺寸，如 1024*1024（qwen-image 支持像素）或档位 1K/2K/4K；默认由模型决定。
tierNo档位：standard(qwen-image-3.0)/pro(wan2.7-image-pro)/max(qwen-image-3.0-pro)，默认 standard。standard
promptYes图片内容描述。
confirmNo必须为 true 才会实际调用付费生成接口；false 时只返回预计费用。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A4/5.0
Behavior4/5

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

The annotations only indicate non-read-only, open-world, and non-destructive. The description adds concrete behavioral context: the result is persisted locally and the return value is a filesystem path, and it can be previewed inline. This is useful beyond the annotations. It does not mention the cost/confirmation gating, but that is documented in the schema parameter description and does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: one opening phrase states the purpose, and one sentence gives the key output side effect and preview integration. Every sentence carries information and there is no filler.

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 full schema documentation, annotations, and no output schema, the description covers the critical output behavior (local path return) and preview capability. It could be improved by explicitly noting the paid/confirm gating or when to select this vs. the video generators, but those are either in the schema or inferable from the clear text-to-image purpose.

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 description coverage is 100%, so the baseline applies. All four parameters (prompt, size, tier, confirm) are already fully described in the schema. The tool description itself does not add additional meaning about the parameters, aside from loosely implying the prompt is the text-to-image input.

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?

The description clearly states the tool's core function ('文生图' - text-to-image), and adds a distinct behavioral detail: the generated result is downloaded to a local output directory and a path is returned. This differentiates it from the sibling video-generation and recognition tools.

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?

The description implies the main use case (generate an image from text) and mentions inline preview via dsh-image-preview, which gives some context. However, it does not explicitly state when to prefer this tool over generate_video or the recognition tools, nor does it provide any exclusions or alternative routing.

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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