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GenMagic

by io.github.yumaheymans

Generate an image

generate_image

Generate an image from a text description with GenMagic. Pass 'image' (an https or data URL) to transform THAT image instead of generating from scratch (image-to-image), and type "logo" to make a brand mark. Returns the image inline plus a hosted URL. On-brand automatically when the account has brand personalization on.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoImage size, e.g. 1024x1024 (square), 1792x1024 (wide), 1024x1792 (tall).
typeNoSet to 'logo' to generate a brand mark instead of a picture.
imageNoOptional reference image (an https URL or a data:image URL) to transform (image-to-image), e.g. a character to keep consistent.
promptYesA description of the image to create (or the edit to make when 'image' is given).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It usefully states that the image is returned inline plus a hosted URL, and that brand personalization is applied automatically when enabled. However, it does not mention potential account requirements, generation limits, or cost/credit implications, which are relevant for a generative tool with no annotation coverage.

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 three sentences with no wasted words. It front-loads the primary purpose, then covers the two key parameter modes, and ends with return behavior and branding context. It is slightly extended but every sentence earns its place.

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?

For a moderately simple tool with four well-documented parameters and no output schema, the description covers the core operation, return format, mode switching, and brand behavior. It could add explicit guidance about when not to use this tool, but the media-based sibling distinction makes that gap minor.

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?

Schema coverage is 100%, so the baseline is 3. The description adds semantic value beyond the schema by explaining that passing 'image' switches the tool into image-to-image mode rather than generating from scratch, and by clarifying that 'logo' changes the output type. This helps the agent reason about the relationship between prompt, image, and type parameters.

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 states a specific verb and resource ('Generate an image from a text description with GenMagic') and immediately distinguishes it from the sibling generation tools (create_video, generate_music, generate_text, generate_speech) by the output modality. It also clarifies two sub-modes, image-to-image and logo generation, so an agent understands the full scope of the tool.

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 gives concrete usage conditions: pass an 'image' URL to transform that image, and type 'logo' to make a brand mark. It does not name sibling alternatives explicitly, but the sibling tools are different media types, so the intended context is clear enough.

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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TDQS

A4.2/5.0
Disambiguation4/5

Each tool is primarily tied to a distinct modality or lifecycle step: image, music, speech, text, and video submission/polling. The only minor overlap is that generate_text can also produce SVG graphics, which borders on generate_image's territory, but the type parameter and logo hint keep them separable.

Naming Consistency4/5

All tool names are lowercase snake_case verb_noun, which is readable and mostly predictable. The slight inconsistency is that four generators use generate_* while the video counterpart uses create_video and get_video instead of generate_video and check_video.

Tool Count5/5

Six tools are well-scoped for a multi-modal generation server: text, image, music, speech, video creation, and video status. Each tool covers a necessary function without redundancy or bloat.

Completeness5/5

The tool surface covers every advertised generation modality and includes the required async polling lifecycle for video. No obvious dead ends or missing operations are needed for the core generation workflow.

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