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

Annotations already indicate it is read-only and idempotent. The description adds behavioral context by specifying the Lucene query syntax support and the article source (peer-reviewed open-access), which goes beyond the 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?

The description is two sentences, each serving a distinct purpose: stating the tool's function and specifying query syntax. It is front-loaded with the core purpose and contains no redundant information.

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?

Given the presence of an output schema and comprehensive annotations, the description adequately covers the tool's purpose and query capabilities. It could be improved by mentioning result limitations or pagination behavior, but these are covered by the schema.

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?

With 100% schema coverage, the schema already describes each parameter. The description adds value by providing concrete examples of Lucene-style queries ('title:climate', 'abstract:"machine learning"'), enhancing understanding of the 'query' parameter beyond its schema description.

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 tool as searching peer-reviewed open-access articles, with explicit support for Lucene-style field queries. This verb+resource combination is specific and distinguishes it from sibling tools like search_journals or deep_research.

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 states what the tool does but provides no guidance on when to use it versus sibling tools (e.g., deep_research, search_journals). There is no mention of appropriate contexts or exclusions.

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