recommendations_from_lists

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states 'Get paper recommendations' without indicating read-only status, authentication needs, rate limits, or error behavior (e.g., for bad IDs). While the core function is clear, significant behavioral traits are missing.

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 sentences: first defines the action, second suggests use cases. No redundant phrases. Every sentence adds value; the description is as short as possible while conveying 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 tool has 5 parameters (all described in schema), no output schema, and no annotations, the description covers the core purpose but omits details like return format, default output fields, and algorithmic behavior. It is adequate but leaves gaps for an agent to fully interpret invocation results.

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 coverage is 100%, baseline 3. The description adds meaning: 'positive example papers (papers you like)' and 'negative examples (papers to avoid)', which enriches the schema descriptions. This helps agents understand the semantic role of parameters beyond technical definitions.

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's purpose: 'Get paper recommendations based on a list of positive example papers... and optional negative examples.' It uniquely identifies the resource (paper recommendations) and method (list-based positives/negatives), distinguishing it from siblings like recommendations_for_paper (single seed) and paper_search (query-based).

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 usage context: 'Useful for discovering papers in a specific research niche or building a reading list.' This implies when to use it but lacks explicit exclusions or comparisons to alternatives like recommendations_for_paper or paper_search. Clear context, no exclusions.

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