get_unextracted_abstracts

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond those annotations: the selection criteria (has abstracts, missing extracted entities), the entity types involved, and the recommended next step. No contradictions with annotations.

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 concise sentences deliver the core purpose, usage trigger, and follow-up workflow without filler. The most important information is front-loaded in the first sentence.

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?

The tool is simple, has one optional parameter, and an output schema exists. The description explains what papers are returned and how to proceed. The only minor gap is the undocumented limit parameter, but the default of 50 and the intuitive name mitigate the issue.

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 single parameter 'limit' has 0% schema description coverage, and the tool description does not explain its semantics, accepted formats, or behavior. The name is somewhat intuitive, but the schema's anyOf string/integer design is left undocumented, so the description does not compensate for the schema gap.

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 specific action and resource: get papers that have abstracts but no extracted biomedical entities yet. It identifies the exact purpose—finding papers that need entity extraction—and distinguishes this from related sibling tools like store_entities and search_entities by narrowing the resource state.

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 explicitly says 'Use this to find papers needing entity extraction' and provides the intended follow-up workflow with store_entities. There are no explicit when-not-to-use instructions or alternative tool mentions, but the usage context is strong enough for an agent to select this tool appropriately.

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