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

With no annotations, the description carries the full behavioral burden, and it does substantial work: it disclaims what the tool will not accept, clarifies that it is not peer review or accreditation, notes that it reads any language and answers in the text's language, flags the Portuguese/English calibration caveat, and requires an API key. It stops short of covering all operational behaviors, but the key non-obvious traits are disclosed.

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 front-loaded with the core purpose and use cases, and every major claim earns its place. However, the Portuguese trigger phrase and the language-calibration caveat add notable length; they are useful but could be tightened, so it is not maximally concise.

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 5-parameter tool with no output schema and no annotations, the description is fairly complete: it explains what the return value includes, what inputs are excluded, language behavior, and authentication requirements. It does not give an example or detail the DOI qualification logic, but it covers the essential operational context an agent needs.

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 input schema already documents all five parameters including enums for work_type and academic_level. The description adds general context about scoring and language but does not add parameter-specific meaning beyond the schema, so the baseline of 3 is appropriate.

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 resource: 'Score a text against published academic criteria.' It clearly distinguishes evaluate_text from siblings by focusing on evaluation with a reviewable score, whereas get_criteria, screen_text, and verify_record suggest retrieval, screening, and verification rather than scoring.

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 explicit when-to-use guidance ('Use when a user wants to know how strong a paper... or wants a reviewable score before submitting') and strong when-not-to-use boundaries ('Does not accept recipes... This is not peer review and the score is not an accreditation'). It does not explicitly name sibling alternatives, which keeps it from a 5.

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