semantic_scholar_multi_recommend

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

Annotations already mark the tool as readOnly, openWorld, and idempotent. The description adds valuable behavioral context: results are steered toward positives and away from negatives, malformed IDs raise an error before making requests, and the response format can be markdown or JSON. It stops short of discussing auth or rate limits, but the additional context is substantive.

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 tightly written in four sentences, immediately stating the purpose, followed by usage guidance, an example, parameter ranges, and output format. There is no fluff—every sentence contributes essential information.

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 complexity of a multi-seed recommendation tool, the description covers the central behavior, parameter constraints, error handling, and return format. It omits the deprecated api_key parameter, but the schema explains it. The tool has no output schema, so the description effectively fills the gap by stating what is returned (papers, markdown or JSON).

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 already provides descriptions for all fields, so the baseline is 3. The description enhances parameter understanding by showing an example with specific ID formats, reaffirming the 1-100 positive and 1-100 negative limits, and noting the limit/default/response_format options. This adds practical value beyond the schema's static descriptions.

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 opens with a specific verb and resource: 'Get recommendations using multiple positive and negative example papers.' It immediately distinguishes itself from sibling tool semantic_scholar_recommendations by stating it is for 'more than one example,' making the purpose unmistakable.

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 when to use this tool: 'Use instead of semantic_scholar_recommendations when steering with more than one example.' It also provides a concrete example and clarifies input limits, giving clear guidance on when and how to invoke it.

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