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multimodal-mcp

multimodal-mcp

by LinJianKun
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

mm_generate_image

Generate an image from a text prompt, customize size, and receive the absolute file path of the saved output.

Instructions

根据文字提示生成图片并保存。

Args: prompt: 图片内容描述。 size: 画幅，支持 1024x1024 / 768x1344 / 1344x768。 output_path: 可选保存路径（默认 outputs/images/）。

Returns: str: 生成的图片文件绝对路径。

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": "mm_generate_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
sizeNo1024x1024
promptYes
output_pathNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.3/5.0
Behavior4/5

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

The description adds useful behavioral details beyond annotations: it specifies saving to output_path (defaulting to outputs/images/), lists supported sizes, and returns an absolute path. It aligns with readOnlyHint=false without contradiction.

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 concise and well-structured: a one-sentence summary followed by Args and Returns sections. No unnecessary information or repetition.

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?

The description covers all parameters, defaults, and the return type, which is sufficient for a simple image-generation tool. It could mention error behavior or file-overwrite details, but these are not critical for basic invocation.

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

Parameters5/5

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

With 0% schema description coverage, the description fully explains each parameter: prompt (image content), size (supported formats), and output_path (optional, default). This provides necessary meaning beyond the raw schema.

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 generates and saves an image from a text prompt, distinguishing it from sibling tools like mm_generate_video and mm_describe_image.

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 implied from the purpose, but there is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or conditions.

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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