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

With no annotations, the description carries the burden and discloses the time window (last 30 days), the metrics returned (total, latest day, average), and truncation behavior for lists over 20 packages. It could say more about error handling or read-only safety, but the key behavioral constraints are present.

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 a single efficient sentence with the core purpose and constraints front-loaded. Every clause adds useful information without repetition.

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 the output schema exists and the tool has only one parameter, the description provides enough context about scope, metrics, and truncation to call it correctly. The main remaining gap is how to format package identifiers across npm and PyPI.

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 0%, so the description must compensate. It adds the important limit of 20 packages and truncation behavior, but it does not clarify the expected format for package names or how npm versus PyPI is specified.

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 names a specific verb and resource: comparing npm/PyPI download activity for up to 20 packages. It clearly scopes the time window and metrics, though it does not explicitly differentiate itself from siblings like get_npm_trends or get_pypi_trends.

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 states a clear use case: 'Use when the user wants to compare package popularity side by side.' However, it does not mention when not to use it or name alternative tools for single-package trend analysis.

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