get_package_prompt

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

Annotations already establish read-only, open-world, idempotent, and non-destructive behavior. The description adds meaningful context beyond annotations: plain-text output, ~300 tokens, ~75% cheaper than JSON, and the specific content components (verdict, health, vulns, alternatives, maintainer alerts). This is valuable behavioral disclosure.

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 (~75 words) and well-structured: main value proposition first, then usage conditions, preference over alternatives, and return format. 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 two-parameter schema, strong annotations, and no output schema, the description sufficiently explains purpose, output content, and usage context. It covers what the tool returns, why it's useful for LLMs, and when to use it, making it complete for an agent.

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 0% and the description does not explicitly explain the two parameters (package and ecosystem). While the parameter names and enum for ecosystem are self-explanatory, the description fails to compensate for the lack of schema descriptions. It adds no meaning beyond the field names.

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 returns an LLM-optimised package brief with a verdict (SAFE/AVOID/URGENT/MALICIOUS) plus health, vulns, alternatives, and maintainer alerts. It explicitly differentiates from sibling check_package by noting it's plain-text, cheaper, and preferred in 95% of LLM cases.

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?

Provides explicit 'USE WHEN' guidance (reasoning over a package, dropping output directly in context, 'is X safe') and explicitly recommends preferring this over check_package in most LLM scenarios. This gives clear when-to-use and alternative guidance.

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