get_paper_details_s2_tool

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

No annotations are provided, so the description carries the full burden. It discloses it accepts two identifier types and returns metadata with TLDR, but omits behavioral traits such as error handling, rate limits, or authentication requirements. Adequate but not comprehensive.

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 short sentences with no wasted words. The most critical information is front-loaded: 'Get detailed metadata from Semantic Scholar, including the AI-generated TLDR.' Immediate clarity.

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 one parameter and an output schema present, the description covers the essential input details and the core functionality. It lacks information on error conditions or prerequisites, but given the tool's simplicity and the presence of an output schema, it is reasonably complete.

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%, so the description compensates by clarifying that paper_id can be an S2 paper ID or a DOI, providing concrete examples. This adds significant meaning beyond the schema's generic 'Paper Id' label.

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 detailed metadata from Semantic Scholar, including the AI-generated TLDR. It specifies the resource (Semantic Scholar) and the action (get metadata), distinguishing it from sibling tools like get_paper_details_tool which may use a different source.

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 input guidance (accepts S2 paper ID or DOI) but does not specify when to use this tool over alternatives like get_paper_details_tool or search_papers_tool. There is no explicit 'when to use' or 'when not to use' context.

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