Return curated editorial picks across the directory, or within a specific category if provided. Use for 'recommend the best AI tools' or 'top X in Y' questions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No | Optional category linkName to scope the picks. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only restates the core function. It does not describe output format, ordering, how category filtering behaves, or error handling. The 'curated editorial' phrasing adds minimal 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and contains no fluff or repetition. Every phrase adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two optional parameters and no output schema, the description conveys the core purpose and use cases. However, it lacks details about the return structure or what constitutes a 'pick', which would be useful given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 50% of parameters; 'category' is described in the schema, and 'limit' has clear constraints (default, min, max). The description adds usage context ('top X in Y') but does not further explain parameter semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'curated editorial picks' with an optional category scope, which is a specific verb+resource+scope. It distinguishes itself from siblings like search_tools or browse_category by emphasizing the 'editorial' nature of the results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage scenarios: 'recommend the best AI tools' or 'top X in Y' questions. This clearly signals when to use the tool, though it doesn't explicitly mention alternatives or when not to use it.
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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Each tool has a clearly distinct purpose: browsing a category, comparing two tools, finding alternatives, fetching tool details, getting top picks, listing categories, and searching. There is no ambiguity or overlap.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., browse_category, compare_tools, get_tool). The naming is predictable and uniform across the set.
With 7 tools, the set is well-scoped for an AI tool directory. Each tool serves a specific and necessary function, and there is no unnecessary bloat or deficiency.
The tool surface covers all core use cases for a directory: browsing by category, searching, getting details, comparing, and finding alternatives. Minor gaps like missing 'top trending' or user-specific features are acceptable and do not hinder typical workflows.
