Live trending packages with rank-delta and weekly growth %. USE WHEN: 'what is rising in npm/PyPI/Cargo right now'; recommendation not biased by training-data cutoff. RETURNS: {items[]: {name, rank, rank_delta, weekly_growth_pct}}.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results, 1-50. Default 20. | |
| scope | No | Time window. Defaults to week. | |
| ecosystem | No | Optional. If omitted returns cross-ecosystem trending. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description does not need to cover those. It adds valuable behavioral context by stating the data is 'live' and that recommendations are not biased by training-data cutoff, plus it describes the return shape. This goes beyond the annotations without contradicting them.
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 compact and well-structured with a clear lead sentence, a USE WHEN clause, and a RETURNS specification. Every sentence carries information—there is no fluff or repetition—making it easy for an agent to quickly parse.
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 tool with three optional parameters and no output schema, the description provides sufficient context: it explains the use case, the return format, and the live-data characteristic. It could optionally mention pagination or default limits, but those are in the schema, so the description is nearly complete.
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?
All three parameters have descriptions in the schema, so schema coverage is 100%. The tool description does not add extra meaning to the parameters themselves; it only mentions output fields like rank and growth. Since the schema already handles parameter semantics, a baseline of 3 is appropriate.
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 immediately states it returns 'Live trending packages with rank-delta and weekly growth %', which is specific and distinguishes it from sibling tools that focus on vulnerabilities, compatibility, or malware. The USE WHEN clause provides a concrete query ('what is rising in npm/PyPI/Cargo right now') and adds a unique value proposition (not biased by training-data cutoff), making the purpose unmistakably clear.
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?
The USE WHEN clause gives explicit context for when to invoke this tool, which is more than most tool descriptions provide. However, it does not explicitly state when not to use it or mention alternatives among the sibling tools, so it falls short of a perfect 5.
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 targets a specific, well-defined function with clear use cases. Overlapping functions like check_package and get_package_prompt are explicitly differentiated by output format and cost. No tools are ambiguous.
Almost all tools follow a verb_noun pattern (check_, get_, find_, etc.). The only exception is 'package_exists', which uses noun_verb, creating a minor inconsistency. Overall consistent and predictable.
22 tools is slightly above the typical 3-15 range but each tool earns its place by addressing a distinct aspect of dependency analysis. The count is reasonable for a comprehensive security and health tool.
The tool surface covers the full lifecycle of dependency evaluation: existence, health, vulnerabilities, malware, compatibility, migration, trending, trust signals, error resolution, and project scanning. No obvious gaps exist.
