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get_manifest

Retrieve the complete JSON manifest of all 303 curated generative art algorithms, organized by categories like physics, fractals, and cellular automata. Use to list all entries.

Instructions

Return the full Logic Lab art algorithm manifest as a JSON object.

The manifest contains an 'entries' array. Each entry includes:
- path (str): manifest-relative path to the source file (e.g. 'physics/wave/wave.py')
- title (str): human-readable algorithm name
- category (str): domain (physics, steering_behaviors, genetic_algorithms,
  neuro_evolution, fractals, cellular_automata, mathematical, tiling_patterns,
  research, simulation, shader)
- concepts (list[str]): key algorithmic concepts demonstrated
- visual_use (str): one-line description of the visual output
- good_for (list[str]): suggested use-cases and aesthetic tags
- complexity (str): 'low', 'medium', or 'high'
- dependencies (list[str]): required Python packages beyond py5

This tool is read-only and returns cached data for the current session.
Prefer search_algorithms for filtered discovery. Use get_manifest when you
need the full entry list or want to enumerate all available categories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

The description discloses the tool is read-only and returns cached data, which is helpful. However, no annotations are provided, and it does not mention cache freshness or expiration, leaving a minor gap.

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?

The description is front-loaded with a one-line summary, uses a clear bullet list for entry structure, and concludes with usage guidance. Every sentence adds value.

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 that an output schema exists (context signal), the description appropriately focuses on structure and usage. It covers the essential behavioral context (read-only, cached) and entry format, making it complete for a no-parameter tool.

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?

With zero parameters, baseline is 4. The description adds no parameter-specific info, which is acceptable since none are needed.

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 returns the full Logic Lab art algorithm manifest as a JSON object, and explicitly differentiates from search_algorithms by stating when to use each.

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?

It explicitly says to prefer search_algorithms for filtered discovery, and use get_manifest when the full entry list or category enumeration is needed, providing clear when-to-use guidance.

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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