Check Quality

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant detail: specific resource types checked, label lookup across all graphs, and false positive filtering of import stubs, providing behavioral depth beyond annotations without contradiction.

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?

Well-structured with sections for args, returns, when-to-use, notes, and false positive filtering. Front-loaded with purpose. While slightly lengthy, every section adds value with minimal redundancy.

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 quality check tool with no output schema, the description comprehensively covers what is checked, how parameters affect results, false positive handling, and return format. It is complete enough for an AI agent to use correctly.

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 50% (ontologyUri described, limit not). The description adds: default for limit (50), detailed usage of ontologyUri to avoid false positives, and its relationship to explore_ontology. This meaningfully supplements the 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 clearly states it verifies quality issues like missing labels or descriptions, specifying the resource types checked (owl:Class, owl:ObjectProperty, etc.). This distinctly separates it from siblings like 'check_overlaps' or 'check_coverage'.

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 includes a 'When to use ontologyUri' section with explicit guidance on when to pass the ontology URI to exclude imported resources, reducing false positives. It also explains false positive filtering. While it doesn't list alternatives explicitly, the context is clear.

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