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mac_recipe_search

Search built-in and saved macOS automation recipes using natural language. Filter by app and optionally include action history to reuse proven patterns before writing new AppleScript.

Instructions

Search 118 built-in + saved recipes by natural-language query. Call this BEFORE writing new AppleScript.

Examples:

  • { query: "dark mode" } → matches toggle-dark-mode, get-dark-mode

  • { query: "screenshot", app: "Finder" }

  • { query: "send email", includeHistory: true }

Limitations:

  • FTS5 tokenizer is English-biased. For Korean/CJK, supplement with the app filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appNoFilter by target app name
queryYesNatural language search query
includeHistoryNoAlso search raw action history (default: false)
Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses the FTS5 English-biased tokenizer limitation, shows example match behavior for queries, and mentions includeHistory semantics. It does not describe return format or pagination, but for a search tool the examples and limitations provide meaningful behavioral context.

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 concise, well-structured, and front-loaded with the core purpose. Examples are useful and the limitation is stated in one line. Every sentence earns its place without redundant fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a moderately simple search tool with no output schema, the description provides enough to get started: purpose, examples, and a key limitation. It does not disclose what the result payload looks like or how matches are ranked, but the examples and explicit 'before writing AppleScript' context make this acceptable for the tool's complexity.

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 the description enriches parameter understanding with concrete examples: query is natural-language text, app is a filter that also helps with CJK tokenization, and includeHistory expands search to raw action history. This adds value beyond the terse schema descriptions, though the schema already documents the basics.

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 searches 118 built-in and saved recipes using natural language, which is a specific verb+resource pairing. It also differentiates from siblings by emphasizing this is a search step before writing new AppleScript, distinguishing it from mac_recipe_save, mac_recipe_run, and import/export tools.

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 gives a direct usage directive: 'Call this BEFORE writing new AppleScript,' which establishes when to use it. Examples show useful variations with app filtering and includeHistory, and the limitation note advises using the app filter for Korean/CJK. It does not explicitly name alternative sibling tools for when not to use it, but the context is fairly clear.

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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