semantic_scholar_multi_recommend

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

Annotations already indicate readOnly, idempotent, openWorld. Description adds no further behavioral context (e.g., how negative examples affect results, output format details, or any side effects). Adequate but no extra value.

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?

Single sentence, front-loaded with action and resource, 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?

Description is minimal for a tool with multiple nuanced parameters (positive/negative examples). Output schema exists, so return values are covered. However, usage context (e.g., how negative examples work, max/min limits) is missing, leaving gaps for an AI 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% in tool description, but the input schema itself provides detailed parameter descriptions. The tool description adds no additional meaning beyond the schema, meeting baseline but not exceeding.

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?

Description clearly states 'Get recommendations' and specifies using 'multiple positive and negative example papers', distinguishing it from sibling tools like 'semantic_scholar_recommendations' which likely uses a single positive 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?

No explicit when/why to use this tool over alternatives. The name and description imply it's for multi-paper recommendations, but lacks guidance on when to choose this over 'semantic_scholar_recommendations' or other siblings.

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