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

Beyond the read-only/idempotent annotations, the description discloses important non-obvious behavior: the exact source chain, per-copy license handling ('never collapses free to read into permitted to reuse'), and independent leg failures being reported in sources_failed instead of failing the call. This is rich behavioral context.

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 dense yet efficiently structured: core value proposition first, then the chain via arrows, then use cases, then failure behavior. Every sentence earns its place with no filler while remaining scannable.

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 no output schema, the description carries the burden of explaining return expectations. It lists all major output categories (full text, license, datasets, code, citations, retraction status) and even explains fallback behavior. An agent has enough information to call the tool correctly and interpret results at a high level.

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 100%, so the schema already documents all parameters and their formats. The description adds high-level context on how identifiers are resolved but does not add new parameter-level semantics beyond what the schema provides, matching the baseline of 3.

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: it 'walks paper' and aggregates full text, license, datasets, code, citations, and retraction status. This clearly distinguishes it from siblings like deep_research or search_within by scoping exactly to a paper's open ecosystem.

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?

Provides explicit use-for examples such as 'what datasets/code are linked to <DOI>' and 'find an open copy of <DOI> and its license', giving clear context for when to invoke it. It does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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