Get Content Recommendations

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'relevance scoring' which hints at output characteristics, but fails to detail any side effects, filters, defaults, or that the operation is read-only. The description is too sparse to adequately disclose behavioral traits beyond the core function.

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 a single sentence that is front-loaded with the core purpose ('Get personalized content recommendations') and adds a distinguishing detail ('relevance scoring'). It is appropriately concise with zero wasted words.

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?

With 8 optional parameters, no output schema, and no annotations, the one-sentence description leaves significant gaps. It doesn't explain how recommendations are generated, what the response looks like, or how parameters like similarity_threshold and recency_preference affect behavior. This is inadequate for a complex tool, especially compared to the detailed schema.

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 8 parameters well-documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides. This meets the baseline of 3, as 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 retrieves personalized content recommendations with relevance scoring. This distinguishes it from sibling tools like search_content (query-based) and get_content_feed (feed-based). The verb 'Get' and specific resource 'personalized content recommendations' are concise and unambiguous.

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 implies usage for obtaining personalized recommendations, but does not explicitly contrast with alternative tools (e.g., search_content vs. get_content_feed) or specify when this tool should be preferred. No exclusions or prerequisites are provided, so the guidance is inferred rather than explicit.

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