Python Package Runtime Test

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

The description mentions 'empirical execution' and returning 'scoped install evidence' and 'usable-import evidence', but it does not clarify side effects such as whether it installs into the current environment, uses isolated packaging, makes network calls, or leaves artifacts behind. With no annotations, this missing detail reduces transparency.

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 concise and well-structured: it states the purpose, the returned evidence, the supported boundary, and the exclusions in a compact, logical order. No redundant or filler content appears.

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?

Despite having no output schema or sibling tools, the description provides enough context for an agent to decide whether to call it: what it does, what it returns, its supported scope, and what it does not cover. This fully equips the agent for decision-making.

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 descriptions cover both parameters (requirement and import_targets) fully, and the description text adds valuable constraints: it gives concrete examples (e.g., 'packaging==24.2') and explicitly forbids URLs, VCS references, local paths, and alternate indices. This exceeds baseline coverage, making parameter usage unambiguous.

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 purpose: testing a specific Python package/version on a supported runtime via empirical execution. It also specifies the exact scope (public PyPI, Linux x86_64, CPython 3.13) and explicitly lists what it is not, so an agent can precisely understand when this tool applies.

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 boundaries for use: it supports only public PyPI on a particular runtime and excludes generic package advice, security verification, broad compatibility, and other scenarios. This gives clear guidance on when to (and when not to) invoke the tool, even in the absence of sibling tools.

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