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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuine behavioral value by specifying the returned facets (yank/deprecation status, advisories, runtime requirements), which is important since no output schema exists. It omits any mention of caching, rate limits, or behavior when the package is unknown, but that is a minor gap.

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 dense and front-loaded: the return contents come first, the usage trigger second. Every listed item (version, publish date, yank status, engines, peer deps, license, advisories) earns its place, and there is no filler.

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 two-parameter, fully-required read-only lookup with no output schema, the description compensates by enumerating exactly what comes back, and the usage sentence covers the decision context. Nothing needed to invoke it correctly is missing.

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%, with both params documented and the ecosystem enum constrained, so the schema carries the parameter burden. The description only echoes 'npm or PyPI package' and adds no syntax, scoping, or edge-case guidance beyond the schema.

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 the resource (an npm or PyPI package) and enumerates the specific facts returned: latest stable version, publish date, deprecation/yank status, engine requirements, peer dependencies, license and advisories. An agent can distinguish this from siblings like find_package (search) or check_dependencies (dependency analysis), though it never names an alternative explicitly.

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 gives a clear when-to-use trigger: 'Use before recommending, installing or pinning a package version.' That frames the decision context well, but it offers no explicit when-not condition and does not route the agent to find_package or check_dependencies for related needs.

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