PyPI Package Versions

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, establishing safe read-only behavior. The description adds behavioral specifics: returns the 50 most recent versions (not all historical versions) and lists specific data fields, complementing annotations without contradiction.

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 extremely concise (two sentences) with the core action and key data first, followed by use cases. Every sentence adds value with no 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 the simple tool (one parameter), rich annotations, and existence of an output schema, the description fully informs the agent about purpose, data returned, behavior (50-item limit), and use cases. No gaps remain.

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 100% and the description does not add significant meaning beyond the schema's parameter description. The description focuses on return data rather than parameter details, so it meets the baseline for high coverage without extra value.

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's action ('List all published versions of a Python package') and specifies key data returned (upload dates, yanked status, distribution file types). This distinguishes it from siblings like pypi.packages.info, which likely provides package metadata rather than version history.

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 explicit use cases ('dependency auditing, version pinning, and upgrade planning') that guide when to invoke this tool. However, it does not mention when not to use it or directly compare with siblings like pypi.packages.info.

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