list_products

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

No annotations are provided, so the description carries the full burden. It discloses behavioral traits: it's a read operation ('Browse', 'Returns'), implies no destructive actions, and describes the return content (product names, descriptions, basic metadata). However, it lacks details on rate limits, authentication needs, or pagination behavior, which are common gaps for list tools.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by usage guidelines and return details. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 tool's low complexity (1 optional parameter, no output schema), the description is reasonably complete. It covers purpose, usage, and return values, compensating for the lack of annotations. However, it could improve by mentioning sibling tools explicitly or detailing error cases, keeping it from a perfect score.

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?

The input schema has 100% description coverage, with the 'full' parameter well-documented in the schema itself. The description adds no parameter-specific information beyond what the schema provides, such as clarifying the 'full' flag's impact on performance or use cases. Baseline 3 is appropriate since the schema does the heavy lifting.

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: 'Browse all software products tracked for end-of-life information' (specific verb+resource). It distinguishes itself from siblings by focusing on comprehensive listing rather than filtered queries (e.g., get_category_products, get_product). However, it doesn't explicitly contrast with all siblings like list_categories or list_tags, keeping it from a perfect score.

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 clear usage context: 'Use this as your starting point to discover what products are available, or when you need to help users find software they're asking about.' This indicates when to use it (initial discovery, user assistance) but doesn't explicitly state when not to use it or name specific alternatives among siblings, such as get_product for detailed single-product queries.

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