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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions differing backend coverage but omits whether the call is read-only, pagination/limit behavior, project scoping requirements, rate limits, or the cost of switching sources. The '(#3596)' ticket reference adds no behavioral 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?

Two short sentences, front-loaded with the core action and the backend choice. The trailing '(#3596)' issue reference is pure noise and should be removed.

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?

An output schema exists, so return values need no explanation. But with zero schema description coverage across four parameters and no differentiation from the paper-related siblings, the definition leaves an agent unable to call this correctly without inspecting the schema and guessing at the sibling boundary.

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 must compensate for four undocumented parameters. It clarifies only 'source' (enum values plus the OpenAlex default); 'query', 'limit', and the required 'project_id' are left completely unexplained.

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 states a specific verb and resource ('Search academic papers') and names the two backends it queries. It does not distinguish itself from the sibling 'list_papers' or 'read_paper', so an agent must infer the boundary between searching and listing.

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?

It gives a conditional usage hint for one parameter value ('Use source="semantic_scholar" for broader AI/CS preprint coverage and citation-influence metrics'), which implies when to prefer that backend. However, it says nothing about when to use search_papers vs siblings like list_papers, nor about any prerequisite conditions.

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