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make_carousel

Split one wide photo into seamless slides for an Instagram carousel and get one download link per slide, in order. Pick slides (2 to 10; default: as many as the photo's width allows at its full height) and ratio ('4:5' = 1080x1350, the default, or '1:1' = 1080x1080). The server crops a strip of N slides, centred on the main subject when it can find one (else the middle), scales it once to N x 1080 wide (never up: a smaller photo keeps its own size), cuts it into N exact slides and exports each, so the slides join without a seam when swiped. Your photo is left as it was; the result gives its (new) image id. Each slide is cut from a copy of the strip, so a 10-slide carousel takes a while.

Input Schema

NameRequiredDescriptionDefault
imageYesImage id, as returned by open_photo.
ratioNoSlide shape: 4:5 (1080x1350) or 1:1 (1080x1080).4:5
formatNoOutput format.jpg
slidesNoHow many slides. Default: as many as fit the photo's width at its full height.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). It discloses non-obvious behaviors: the origin image is left intact, the result returns a new image id, the subject-centered crop logic, no-upscaling constraint, seamless joining mechanism, and a performance warning for 10-slide carousels.

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?

Well-structured and front-loaded with the core action and return value, then parameters, then implementation details. It's dense but every sentence provides actionable information. Slightly long, but the length is justified by the non-trivial behavior being disclosed.

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 the complexity of the operation, no output schema, and rich annotations, the description covers everything an agent needs: input requirements, parameter behavior, processing logic, output structure (download links), and a performance caveat. Nothing critical is missing.

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?

Adds meaning beyond the 100% schema coverage: explains the '4:5' and '1:1' ratios with pixel dimensions (already in schema but reinforced), and clarifies what the default slide count means (as many as the photo's width allows at full height). The 'format' parameter is left to the schema, which is fine given full 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?

States a specific verb (split) and resource (wide photo into seamless slides) with the exact outcome (one download link per slide, in order). This is clearly distinguishable from siblings like make_grid, make_story, and crop_for_platform.

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 photo requirements (wide photo) and use case (Instagram carousel) are clear, and the parameter behavior is well-explained. However, it doesn't explicitly contrast with alternatives like make_grid or crop_for_platform, leaving the agent to infer when this specific approach is preferred over 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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