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

Since there are no annotations, the description carries the full behavioral burden. It is highly transparent by detailing exactly what the agent can expect to find in the output, such as 'top three breaking headlines' and 'verdict.' This information greatly aids the agent's expectation setting.

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 definition is efficiently written, dedicating each clause to a specific piece of crucial information. It avoids fluff and immediately communicates the core value proposition and its limitations.

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 tool's function as a summary check, it is extremely complete. It covers the purpose, the precise metrics returned, and the pathway to the deeper, paid analysis, leaving no ambiguity for an agent wishing to use it correctly.

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?

Although the schema coverage is 100%, the description adds significant semantic value by framing the context—that this tool is designed for libraries potentially known only from 'older training data.' It also clarifies that 'package' should be an npm/PyPI or source id name.

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 defines the tool's purpose as checking if a library has changed since a specific version. It provides concrete output types (verdict, breaking changes, security fixes), effectively distinguishing it from other sibling tools like upgrade_check.

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 specifies the ideal use case is checking libraries known only from older training data. Furthermore, it explicitly directs the user to the alternative, paid tool (upgrade_check) when full evidence is required, defining a clear boundary.

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