generate_scopus_query

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

With no annotations present, the description carries the behavioral disclosure burden. It discloses important behavior: OR-logic union across filters, automatic duplicate ISSN removal, and the inclusive/exclusive semantics of operators. It does not mention output format or error behavior, but the core query-generation logic is transparent.

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 organized with a clear purpose statement, concise bullet lists, and a relevant example. Every sentence adds useful information, and the JSON example is an efficient way to communicate the expected input shape.

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?

For a tool with no output schema and no annotations, the description covers the ranking systems, grade semantics, operator behavior, filter combination, and duplicate handling. It does not describe the output format, but that is a minor gap given the clear generate-a-query purpose.

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 coverage is 100%, so the baseline is 3, but the description adds meaningful value by spelling out grade sets per system, the grade-ordering semantics of 'at_least', and how filters combine. The worked example also clarifies the nested filter structure beyond the raw schema.

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 opens with a specific verb and object: 'Generate a Scopus boolean query of ISSN numbers based on journal rankings.' It further clarifies scope by enumerating the three supported ranking systems, which clearly distinguishes this tool from siblings like list_grades and list_fields.

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 gives clear operating context: how to use 'at_least' vs 'exactly', how multiple filters combine, and a complete example. It does not explicitly contrast with sibling tools or state when not to use it, but the purpose is distinct enough that an agent can infer the right scenario.

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