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ModelScope MCP Server

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Generate Image

generate_image

Generate images from text prompts using ModelScope AI models. Supports text-to-image and image-to-image, with an optional source image for transformation.

Instructions

Generate an image based on the given text prompt and ModelScope AIGC model ID.

Supports both text-to-image and image-to-image generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoThe model's ID to be used for image generation. If not provided, the default model for the corresponding generation type (text-to-image or image-to-image) is used.
promptYesThe prompt of the image to be generated, containing the desired elements and visual features.
image_urlNoThe URL of the source image for image-to-image generation.If not provided, performs text-to-image generation.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesType of image generation
modelYesModel used for image generation
image_urlYesURL of the generated image
Behavior3/5

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

Annotations are minimal (only destructiveHint: false), leaving the description to carry transparency. The description adds the key detail of dual-mode generation but does not disclose potential side effects such as computational cost, latency, or whether the generated image is returned as a URL or file. It doesn't contradict annotations, but the provided behavioral context is modest.

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 two sentences, front-loaded with the main action ('Generate an image'), and contains no filler words. Every phrase earns its place, and the structure is easy to parse quickly.

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 the essential functionality (both generation modes) and the schema covers all parameter details. An output schema exists so return values need not be described. It does not mention any prerequisites or limitations, but for a relatively simple generation tool, the information provided is sufficient for an agent to select and invoke it correctly.

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 each parameter is already well-documented. The description adds no significant semantic detail beyond what the schema provides, only re-confirming the existence of text-to-image and image-to-image modes. Baseline 3 is appropriate given full schema coverage.

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 function: generate an image based on a text prompt and model ID. It explicitly mentions both text-to-image and image-to-image modes, making the purpose specific and easily distinguishable from sibling tools (which are all search/info tools).

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool (generating images) and describes two usage modes (text-to-image and image-to-image) which implies the role of the image_url parameter. While it doesn't explicitly state 'use this instead of X', none of the sibling tools overlap in functionality, so the guidance is adequate.

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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