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Glama

backgrounds

by app.creatica

Generate a background

generate_background
Read-onlyIdempotent

Render a background as SVG or PNG. Deterministic: the same structure, options and seed always give the same image, so store the returned seed. Returns a permanent, cacheable image URL you can use directly in /CSS, plus an inline PNG preview. Call describe_background first to learn which shape options the structure honours.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoAny string. Omit for a fresh random seed (returned in the result).
brandNoSlug of a saved brand kit (see list_brand_kits); its colours become the palette baseline.
shapeNoStructure-specific options; call describe_background for the exact keys, ranges and what they do.
widthNo
formatNopng (default) returns a viewable image; svg returns the vector source.
heightNo
paletteNo
pngWidthNoRaster width for PNG output/preview. Defaults to the render width, capped for inline previews.
structureYesStructure name, e.g. "wave".

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial context beyond the annotations: determinism ('same structure, options and seed always give the same image'), the instruction to store the returned seed, and the caching/permanence semantics of the returned URL. These details align with and enrich the readOnlyHint and idempotentHint annotations without contradicting them.

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?

Three dense sentences with zero filler. Every clause carries information: output formats, determinism, return contract, and the prerequisite sibling call. The most decision-relevant facts are front-loaded.

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?

Given 9 parameters, nested objects, and no output schema, the description covers the critical unknowns: what the tool returns, determinism behavior, and where to get shape/options details. It does not describe error cases or behavior for unknown structure names, but for selection and initial invocation this is adequately complete.

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?

At 67% schema coverage the schema does much of the work, but the description adds real meaning where it matters most: it explains the seed's role in determinism ('store the returned seed') and routes the agent to describe_background for the opaque, structure-specific shape object. This compensates for the least-documented 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?

States a specific verb ('Render') plus resource ('background') and the two output formats (SVG/PNG). It also names the return contract (permanent URL + inline preview), which distinguishes it clearly from the describe/list/suggest siblings.

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?

Gives an explicit precondition and sibling pointer: 'Call describe_background first to learn which shape options the structure honours.' This tells the agent the correct workflow before invoking. It lacks explicit when-not-to conditions against list_backgrounds or suggest_backgrounds, but the core usage context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All backgrounds toolsInspect
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TDQS

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct task: browsing catalog, inspecting a structure, generating output, listing palettes, listing brand kits, and suggesting options. list_brand_kits and list_palettes are the only superficially similar pair, but their descriptions clearly separate saved account kits from built-in palettes.

Naming Consistency5/5

All tools use the same verb_noun snake_case convention: describe_, generate_, list_, and suggest_. Pluralization is consistent within each verb's usage, making the naming predictable and easy to infer.

Tool Count5/5

Six tools is well-scoped for a background generation service. Discovery, description, generation, suggestion, and color inputs are each represented without redundancy or excessive surface area.

Completeness5/5

The tool set covers the full consumer workflow: browse backgrounds, understand a structure, generate an image, and supply palettes or brand kits. The only potential gap, managing brand kits, is outside the server's stated purpose.

Resources