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

Annotations only cover read-only/idempotent/non-destructive hints. The description adds valuable behavioral disclosures: 'No code execution' addresses a safety concern, and the $0.003 cost plus payment instructions are critical for an agent deciding to call it. It also clarifies no provider account is required, exceeding the annotation baseline.

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?

Three sentences with zero fluff. The core purpose is front-loaded, followed by critical cost and security notes. Every sentence earns its place.

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 simple one-parameter tool with no output schema, the description fully covers what data is returned, cost, execution safety, and authentication requirements. No gaps remain for an agent to call 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?

Input schema covers 100% of the parameter with a clear description ('Public Python package name'). The description does not add new parameter-level details like case sensitivity or formatting, so it stays at the baseline for high schema coverage.

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?

Description gives a specific verb ('Inspect') with a resource ('Python package') and enumerates exact data fields (latest release, Python compatibility, dependencies, license, advisories). The Python-specific scope clearly distinguishes it from siblings like get_npmsearch_report and get_package_report.

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 makes the tool's context obvious (Python packages on PyPI) and implies it is the appropriate choice for Python package metadata. It does not explicitly name alternative tools or exclusions, but the python-specific scope and sibling names provide sufficient guidance.

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