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recommend_combinations

Suggests ranked multi-layer algorithm combinations for a given artistic intent, returning recipes with layers, moods, and descriptions to plan generative artworks.

Instructions

Suggest multi-layer algorithm combinations for a given artistic intent.

Returns a dict with:
- intent (str): the original intent string
- combinations (list): ranked list of layered recipes
- tip (str): guidance for following up on returned paths

Each combination includes:
- name (str): recipe name
- description (str): recipe description
- moods (list[str]): associated creative moods
- layers (list): each layer has role (str), query (str), and suggestions
  (list of manifest entries resolved by search_algorithms)

Layer roles describe compositional function (e.g. background, agents, texture,
overlay). Suggestions are live manifest entries — use get_algorithm_summary or
get_algorithm on any suggested path for full details.

Use this tool to plan layered generative artworks from a text description.
It combines curated recipes with dynamic algorithm lookup per layer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesFree-text description of the artistic intent or visual goal (e.g. 'cosmic void with particle trails', 'organic growth with geometric structure', 'flowing smoke with invisible force fields'). Used to rank curated multi-layer recipes by keyword relevance.
countNoNumber of combination recipes to return. Accepts integers in the range 1 to the total number of available recipes. Default: 3. Recipes are ranked by how closely their name, description, moods, and layer queries match the intent.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden for behavioral transparency. It thoroughly explains the return structure (intent, combinations, tip) and the composition of each combination (name, description, moods, layers). It notes that suggestions are live manifest entries, implying a read operation. However, it omits details like authorization requirements or rate limits, but given the nature of the tool (recommendation, no side effects), this is acceptable.

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?

The description is well-structured with clear sections: main purpose, return fields breakdown, and usage instruction. It is relatively long but each sentence adds value (explaining structure, roles, follow-up actions). Minor redundancy could be trimmed, but overall it is appropriately sized for the complexity of the output.

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 existence of an output schema (not shown but stated as present), the description still explains the return structure in detail, covering all fields and sub-fields such as 'layers' with their components. It also provides context on how the tool works (curated recipes, dynamic algorithm lookup). This makes the description complete for the tool's complexity.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant value beyond the schema. For 'intent', it provides examples ('cosmic void with particle trails') and explains its role in ranking. For 'count', it clarifies default, acceptable range, and ranking mechanism. This extra context helps the agent understand parameter semantics beyond the schema's bare definitions.

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 clearly states the tool's purpose: 'Suggest multi-layer algorithm combinations for a given artistic intent.' It specifies that it returns ranked combinations with structured details. The distinction from sibling tools (e.g., search_algorithms, get_algorithm) is implicit but clear, as this tool focuses on curated layered recipes with dynamic lookup per layer.

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 provides explicit use-case guidance: 'Use this tool to plan layered generative artworks from a text description.' It also advises following up with get_algorithm_summary or get_algorithm for details. While it does not explicitly state when not to use this tool or compare directly with alternatives, the context of sibling tools makes the usage straightforward.

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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