semantic_scholar_multi_recommend

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. Description adds valuable behavioral context: malformed IDs raise an error before any request, effect of positive/negative steering, and default response format. No contradictions.

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?

Two concise paragraphs with front-loaded main purpose. Every sentence adds value: usage guidance, example, constraints, and error behavior. No 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?

Covers input constraints, differentiation from sibling, error behavior, and response options. Output schema exists and provides return value details, so description does not need to elaborate. Complete for the tool's complexity.

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?

Input schema already provides detailed descriptions for each parameter. The description adds critical behavioral info (error handling for malformed IDs, supported ID formats) that enhances understanding beyond the schema. Schema coverage is effectively high despite context signal stating 0%.

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?

Clearly states 'Get recommendations using multiple positive and negative example papers,' specifying the action and resource. Distinguishes from sibling semantic_scholar_recommendations by noting when to use this variant (more than one example).

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?

Explicitly directs to use this tool instead of semantic_scholar_recommendations when steering with more than one example. Provides a concrete example and specifies input constraints (1-100 positive, up to 100 negative IDs).

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