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Suggest backgrounds

suggest_palette
Read-only

Suggests background palettes for a still or film project, derived from the accent color of its screenshots (or accent) and each already reviewed against this scene for contrast with the devices and texts. Use it once the screenshots are on the screens and before adding texts; list_compositions has the full fixed palette list. Read-only: apply one with edit_scene {op:"set_background", palette:""}. Returns the accent and palettes, best first, each with id, name, gradient, its review result and the ready-to-send op.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accentNoOverride the accent color (#hex), e.g. the brand color.
project_idYesProject id (from create_project or list_projects).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), and the description adds real substance: palettes are pre-reviewed for contrast against devices/texts, results are best-first, and it hands back a ready-to-send edit_scene op. It doesn't state cost/latency or whether the suggestion is cached, so not quite a 5.

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?

Front-loaded with purpose, then timing, then the apply path and return shape. It runs a bit dense in the middle sentence, but every sentence carries information an agent needs; nothing is redundant 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?

With no output schema, the description compensates by naming the returned fields (accent, palettes with id, name, gradient, review result, ready-to-send op) and the ordering. Combined with annotations and full schema coverage, an agent has everything needed to call and consume the result.

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%, so both parameters are already documented in the schema. The description reinforces that `accent` is an override for the derived accent color, but adds no syntax or constraint beyond the schema's '#hex' example. Baseline 3 is appropriate.

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 and resource ('suggests background palettes for a still or film project') plus the derivation source (accent color of screenshots). It also distinguishes itself from list_compositions, which 'has the full fixed palette list,' so an agent can route correctly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit ordering rule ('once the screenshots are on the screens and before adding texts') and names the alternative source of palettes (list_compositions). The follow-up path (apply via edit_scene) is spelled out too.

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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