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backgrounds

Describe a background

describe_background
Read-onlyIdempotent

Full style card for one structure: what it looks like, mood, where text can go, tips, and every parameter it honours with ranges, defaults and the visual effect of low/high values. Also returns the JSON Schema for a generate request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesStructure name from list_backgrounds, e.g. "wave".
formatNomarkdown (default) is best for reading; json includes the JSON Schema.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds a substantial output contract: the style card contents and the JSON Schema for generate requests. Since there is no output schema, this return-value transparency is valuable, though error behavior is not discussed.

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 carry the full message with a front-loaded core deliverable. The colon-delimited list in the first sentence packs useful detail without padding, and the second sentence adds the JSON Schema behavior without any filler.

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?

For a read-only lookup operation with two parameters, one required, and no output schema, the description is complete. It explains what the result contains, how format affects the response, and what the name refers to. The sibling-routing nuance is a usage-guidance issue rather than a completeness gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: 'name' identifies the structure and its source, and 'format' documents the enum, default, and best use for each value. The description itself adds little about the tool's own parameters beyond noting that JSON Schema is returned, so the baseline of 3 applies.

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 deliverable: a full style card for one structure, covering appearance, mood, text placement, tips, and parameter ranges/defaults/effects. It also returns the JSON Schema for a generate request. This clearly distinguishes it from siblings like list_backgrounds (listing structures) and generate_background (creating a 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 gives clear context: it is meant for inspecting a single known structure, and the returned JSON Schema is explicitly tied to a generate request, which implies use before generation. It does not explicitly name alternatives or provide when-not-to-use guidance, so it stops short of a 5.

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

A4.3/5.0
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