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Split a sprite sheet into individual PNGs

split_spritesheet
Read-onlyIdempotent

Cut a packed sprite sheet / texture atlas into separate transparent PNGs, returned as a zip. Give rows and cols for a uniform grid, or omit them to auto-detect sprites by transparent gaps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic URL of the sprite sheet image.
colsNoGrid columns.
rowsNoGrid rows. Omit together with cols to auto-detect.
marginNoPixels to trim inside each grid cell (for sheets with padding).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: the output is a zip file, auto-detection works by transparent gaps, and margin trims inside grid cells. These details go beyond the annotations and help an agent understand the tool's behavior.

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?

Two sentences with no fluff. The first sentence states the action and output; the second explains the two operational modes concisely. It is front-loaded and every word earns its place.

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?

The description, combined with the rich annotations and output schema, fully equips an agent to invoke the tool: it explains the key decision (grid vs. auto-detect), the output format (zip), and the margin option. No critical information is missing given the tool's complexity.

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 enriches the parameter meanings by explaining the relationship between rows/cols and uniform grid mode, and the auto-detect mode when omitted. It also gives purpose to 'margin' (trimming padding), adding value over the plain schema descriptions.

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 uses a specific verb ('Cut') and resource ('packed sprite sheet / texture atlas') and clearly states the output ('separate transparent PNGs, returned as a zip'). This clearly distinguishes it from sibling tools like make_badge, make_chart, etc., which serve entirely different purposes.

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?

It provides clear context on when to use the grid-based approach (provide rows/cols) versus auto-detection (omit them). It also explains the margin parameter for padding. While it does not explicitly compare to sibling tools, they are all unrelated so no exclusion is needed; the context is sufficiently clear.

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

A4.4/5.0
Disambiguation5/5

Each tool produces a distinct output type—badge, chart, QR code, diagram, spritesheet, or AI-generated image—so there is no overlap. The polling utility check_job is clearly separate from the generation tools.

Naming Consistency3/5

Three tools share the 'make_' prefix (make_badge, make_chart, make_qr), but others use different verbs (render_diagram, split_spritesheet, text_to_image, check_job). The naming is understandable but not uniformly consistent.

Tool Count5/5

With seven tools, the server is well-scoped for an image generation service. Each tool covers a specific image type or utility, and none seems superfluous.

Completeness4/5

The set covers a broad range of image generation needs (badges, charts, QR codes, diagrams, sprite sheets, AI images) and includes a job-polling mechanism for async operations. Minor gaps include lack of image editing tools (resize, crop, format conversion) and check_job referencing tools not in this server.