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

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context (cost via x402, live source, time windows) but does not mention rate limits, pagination, or output format. For a simple read tool, this is adequate but not rich.

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, front-loaded with the core purpose, cost, and data source. Every word earns its place; no fluff or redundancy.

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?

Given no output schema and no annotations, the description covers the main purpose and parameters but omits details about the response format and the x_payment parameter. It's a compact, all-in-one description but leaves the agent guessing about return structure and payment mechanics.

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 coverage is 50%, with 'top' and 'since' described in the schema. The description adds meaning for 'language' ('optional language filter') but leaves 'x_payment' entirely unexplained. It partially compensates for the coverage gap but does not fully clarify the payment parameter.

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 top trending GitHub repositories by stars over day/week/month with optional language filtering. It uses a specific verb ('top trending') and resource ('GitHub repositories'), and clearly distinguishes from sibling tools like hn_top_stories or web_search by focusing on GitHub trending data.

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 description provides clear context: time periods (day/week/month), optional language filter, and that it's live from GitHub Search API. While it doesn't explicitly name alternatives or exclusions, the context is sufficient for an agent to understand when to use this tool versus a general web search.

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