Skip to main content
Glama

search_algorithms

Search 303 curated generative art algorithms by keyword, category, or visual intent to discover matching entries with relevance scoring and metadata.

Instructions

Search the Logic Lab manifest for algorithms by keyword, category, or visual intent.

Returns a list of manifest entries sorted by relevance score. Each entry includes
path, title, category, concepts, visual_use, good_for, complexity, and dependencies.
Returns an empty list when no entries match — this is not an error.

This tool returns manifest metadata only; it never reads source files.
Synonym expansion is applied automatically so queries like 'flow' also match
'fluid' and 'stream'. Combining query with category narrows results to a
specific domain.

Recommended workflow: call this tool for discovery, then get_algorithm_summary
for short context on candidates, then get_algorithm only for paths you intend
to use.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search terms matched against title, category, concepts, visual_use, and good_for fields. Use short descriptive phrases such as 'flow field particles', 'recursive tree', or 'emergent flocking'. Synonym expansion is applied automatically (e.g. 'flow' also matches 'fluid').
categoryNoExact category filter (case-insensitive). Limits results to a single domain. Available values: physics, steering_behaviors, genetic_algorithms, neuro_evolution, fractals, cellular_automata, mathematical, tiling_patterns, research, simulation, shader. Omit to search across all categories.
limitNoMaximum number of results to return. Accepts integers in the range 1–50. Default: 5. Results are sorted by relevance score descending.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully carries the behavioral burden. It discloses that the tool never reads source files, applies automatic synonym expansion, and returns an empty list for no matches (not an error). These traits are clearly stated and not contradicted by any annotations.

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 the core purpose and uses short, direct sentences. It efficiently covers all key aspects without redundancy. Every sentence adds value, and the structure is easy to scan.

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 tool has 3 parameters (1 required) and an output schema exists, the description provides comprehensive context: behavior, return shape, synonym expansion, category values, limit constraints, and a recommended workflow. There are no gaps for an agent to misinterpret the tool's capabilities.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining synonym expansion (e.g., 'flow' matches 'fluid') and providing usage context for the query parameter. It also clarifies that category is case-insensitive and lists available values. The limit parameter's range and default are restated, but the workflow context enriches understanding beyond the schema.

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 explicitly states the tool searches the Logic Lab manifest by keyword, category, or visual intent. It clearly indicates what it returns (manifest entries with relevance score) and what it does not (read source files). The recommended workflow distinguishes it from siblings like get_algorithm_summary and get_algorithm.

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?

The description provides explicit usage guidance: use for discovery, then follow with get_algorithm_summary and get_algorithm. It explains how to narrow results by combining query with category, and clarifies that an empty list is not an error. This helps the agent decide when to use this tool vs alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/asamiile/logic-lab'

If you have feedback or need assistance with the MCP directory API, please join our Discord server