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get_algorithm_summary

Retrieve a short summary of a generative art algorithm including metadata and README excerpt to assess relevance without fetching full source code.

Instructions

Return a short summary of a Logic Lab algorithm without fetching full source.

For paths in the manifest, returns all metadata fields:
- path, title, category, concepts, visual_use, good_for, complexity, dependencies
- readme_excerpt: first ~6 non-empty lines of the nearest README.md (up to 1200
  chars) when a README.md exists in the same directory

For paths not in the manifest, returns a minimal summary derived from the file
path (title inferred from directory name, category from the first path segment)
plus readme_excerpt when available.

This tool never returns source code — call get_algorithm for that. Use this
tool to assess relevance before committing to a full source fetch. It is
cheaper in context than get_algorithm for files you may not end up using.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesManifest-relative path to a .py or README.md file (e.g. 'physics/wave/wave.py'). Must be a relative path within the Logic Lab repository. Use search_algorithms to discover valid paths.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses return format for manifest vs non-manifest paths, explicitly states it never returns source code, and indicates it is cheaper. No annotations, so description carries full burden. Could mention error handling but overall transparent.

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?

Well-structured with clear sections, front-loaded purpose, each sentence adds value, no redundancy. Efficient use of space.

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?

Comprehensive given single parameter and presence of output schema. Covers both manifest and non-manifest cases, return fields, and directs to sibling tools for further needs.

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%, but description adds meaning beyond schema: explains how path validity affects output, and suggests using search_algorithms to discover valid paths. Adds context to parameter semantics.

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?

Clearly states the tool returns a short summary without full source, and distinguishes from sibling tool get_algorithm by specifying what it returns (metadata, readme excerpt) and that it never returns source code.

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?

Explicitly tells when to use (assess relevance before full fetch, cheaper) and when not (need source code, then use get_algorithm). Provides clear alternatives.

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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