get_recommendations

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

With no annotations, the description carries the full burden. It mentions personalization and algorithm support but does not disclose side effects, required authentication, rate limits, or what the response contains. The behavioral traits beyond parameters are essentially absent.

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 two short sentences with no wasted words. It is front-loaded with the primary action and resource, making it easy to scan.

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?

The tool has six parameters, no output schema, and no annotations, yet the description is minimal. It lacks any information about return structure, defaults, or how this tool differs from the many sibling recommendation tools, making it incomplete for confident invocation.

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 100%, with all six parameters documented. The description adds little beyond the schema, only vaguely referring to 'multiple recommendation algorithms and filter conditions.' Baseline 3 is appropriate since the schema handles parameter semantics.

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 gets personalized content recommendations with multiple algorithms and filters. However, it does not distinguish this from many similar sibling tools like get_article_recommendations, get_pin_recommendations, or get_trending_recommendations, leaving ambiguity about scope.

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 no guidance on when to use this tool versus the many alternative recommendation tools. It does not mention prerequisites, such as whether user_id is needed for personalization, or when a specific algorithm is preferred.

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