Recommend trending OSS

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

No annotations are provided, so the description carries the full disclosure burden. It does disclose the behavioral basis — the scoring signals (star momentum, download growth, HN buzz) — which is meaningful. But it fails to describe what the agent receives back (list shape, how signals are weighted, or that results are 'established' projects only). The 'limit' param implies 'max established recommendations' but the description never states the output behavior or ranking rationale.

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?

Two sentences, both earning their place — the first states the core purpose and inputs, the second is a high-value usage tip. Front-loaded and free of filler. Slight deduction only because the second sentence's advice partially duplicates schema guidance for the queries parameter.

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 recommendation tool with no output schema and no annotations, the description covers the inputs and scoring basis adequately. But it omits what the agent should expect as output (a ranked list? links? metadata?), whether task alone suffices or queries are needed, and how the three signals are combined. An agent reading this knows what it does but not fully what it returns.

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 description coverage is 100%, so the schema already fully documents all four parameters, which sets the baseline at 3. The description adds marginal value by reinforcing the queries parameter with the 'focused github search terms' tip, but this is a restatement of the schema's own guidance ('2-4 terms each (recommended)') rather than new semantic insight.

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 uses a specific verb-resource pairing: 'recommends trending open-source projects' from a 'task or feature description,' and names the data signals used (GitHub star momentum, npm/PyPI downloads, Hacker News buzz). This clearly differentiates it from siblings like search_repos (searching), repo_health (health), and compare_repos (comparison). It doesn't explicitly name an alternative, but the verb alone distinguishes it — a minor gap from a 5.

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?

It offers a concrete, actionable tip — 'Pass focused github search terms in queries for best results' — which tells the agent how to get good output. However, it provides no when-to-use vs when-not-to-use guidance relative to the sibling tools, and no conditions for choosing this over search_repos or compare_repos. The guidance is procedural rather than decisional.

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