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smythmyke

MarkItUp - AI Image Marketing and Annotation

markitup_generate

Turn any screenshot into marketing visuals with AI-written copy and multiple style templates. Provide an image, describe what to highlight, and get polished variations in one step.

Instructions

Generate polished marketing-visual variations of a screenshot or image using the MarkItUp pipeline (Claude analyzes the image and writes copy; Gemini renders the visuals). Costs 1 credit. Provide the image either as a public URL (image_url) OR as a base64-encoded string (image_base64) — exactly one. Common template IDs: glassmorphic, clean_minimal, bold_marketing, dark_professional, documentation. Returns the generated images plus the marketing copy (headline, subhead) written by Claude.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlNoPublic HTTPS URL of the source image. Mutually exclusive with image_base64.
image_base64NoBase64-encoded image bytes (no data: prefix). Mutually exclusive with image_url.
image_mime_typeNoMIME type when supplying image_base64. Defaults to image/png.image/png
descriptionYesNatural-language description of what the image shows and what should be highlighted or emphasized.
template_idYesTemplate ID controlling visual style. Standard options: glassmorphic, clean_minimal, bold_marketing, dark_professional, documentation.
aspect_ratioNoOptional. One of: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9.
image_sizeNoOptional output resolution tier. Defaults to backend choice.
Behavior4/5

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

With no annotations, the description covers cost (1 credit), the multi-model pipeline (Claude + Gemini), and return value (images + copy), providing adequate behavioral context.

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?

Description is only 3 sentences, each serving a purpose: purpose/pipeline, cost/input constraint, template IDs/output. Perfectly front-loaded.

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

Completeness5/5

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

Given 7 parameters and no output schema, the description covers necessary context: input methods, cost, style options, and what is returned. No gaps.

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%, but the description adds value by clarifying the mutual exclusivity of image_url/image_base64 and listing example template_ids beyond schema defaults.

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 polished marketing-visual variations using a specific pipeline, distinguishing it from siblings like markitup_extend or markitup_remove_background.

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 explicitly says to provide either image_url or image_base64 (exactly one) and lists common template IDs, but lacks explicit when-not-to-use guidance or comparisons to siblings.

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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