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

With no annotations provided, the description carries the full disclosure burden. It successfully identifies Google Scholar as the external data source, but fails to explain pagination behavior (how cursor/page_size interact), rate limiting, or result ordering. It provides minimal viable context but lacks operational details.

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 single sentence is front-loaded and efficient, avoiding redundancy. However, given the 0% schema coverage and presence of pagination parameters, the extreme brevity becomes a liability rather than a virtue, as necessary parameter documentation is sacrificed for conciseness.

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 description covers the core retrieval scenario adequately, and the existence of an output schema absolves it from explaining return values. However, with zero schema descriptions and pagination complexity, the failure to explain cursor-based pagination mechanics leaves a significant gap in contextual completeness.

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%, requiring the description to compensate. While it implies the 'author' parameter through 'filtered by author name,' it completely omits explanation of the pagination parameters 'cursor' and 'page_size,' leaving critical implementation details undocumented.

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 identifies the resource (papers), the filtering mechanism (by author name), and the data source (Google Scholar). It implicitly distinguishes from sibling tool 'search_papers_by_topic' by specifying 'author name' as the filter criterion, though it could be stronger with an explicit comparison.

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 implies usage context through 'filtered by author name,' suggesting when to use the tool (when seeking papers by a specific author). However, it lacks explicit guidance on when NOT to use it or direct comparison to the sibling 'search_papers_by_topic' for topic-based queries.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure but only identifies the data source ('Google Scholar'). It fails to mention pagination behavior, rate limits, authentication requirements, result ordering, or whether results are cached.

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 single sentence contains no redundant words and immediately communicates the core function. However, the extreme brevity contributes to under-specification given the tool's complexity (5 parameters with pagination and filtering).

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?

Despite having 5 parameters (including pagination and date filtering) and an output schema, the description is minimal. It omits critical context for effective use such as pagination mechanics, date filter applicability, and how results are ranked or formatted.

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%, requiring the description to compensate. It only implicitly hints at the 'query' parameter via 'topic keywords' but provides no explanation for 'cursor' (pagination), 'page_size', or the year range filters, leaving significant semantic gaps.

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 verb (Search), resource (Google Scholar papers), and method (by topic keywords). The phrase 'by topic keywords' implicitly distinguishes this tool from the sibling 'get_author_papers', though it does not explicitly name the alternative.

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 no guidance on when to use this tool versus 'get_author_papers', nor does it explain pagination strategy (cursor parameter) or when to apply date filters (year_min/year_max). No prerequisites or exclusions are mentioned.

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