Identify LEGO Item

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

The annotations already provide strong behavioral hints (readOnly, openWorld, idempotent, non-destructive). The description adds valuable context beyond this: it specifies the return format ('top matches with confidence scores, IDs, names, categories, and links to BrickLink/BrickOwl') and mentions the tool's capability to handle multiple item types, which is not covered by annotations. No contradiction with annotations exists.

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 highly concise and well-structured. The first sentence states the core purpose, the second provides usage guidelines, and the third outlines parameters and return values. Every sentence adds essential information with zero waste, making it easy for an AI agent to parse quickly.

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 tool's moderate complexity (image-based identification with multiple item types), rich annotations (covering safety and behavior), and no output schema, the description is largely complete. It explains the purpose, usage context, input requirements, and return format. However, it lacks details on error handling or limitations (e.g., image quality requirements), leaving minor gaps for an agent to infer.

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 schema fully documents both parameters (imagePath and includeRaw). The description mentions imagePath with format details (JPEG, PNG, or WebP) but adds no new semantic information beyond what's in the schema. For includeRaw, it explains its purpose ('useful for debugging'), which slightly enhances understanding but is largely redundant with the schema's description. Baseline 3 is appropriate given high schema coverage.

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 'identify' and the resource 'LEGO item (part, set, minifigure, or sticker)' from a photograph. It explicitly distinguishes this tool from its siblings by stating 'when the item type is unknown or the image may contain multiple types,' contrasting with the specialized sibling tools for specific item types.

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 explicit guidance on when to use this tool: 'when the item type is unknown or the image may contain multiple types.' This clearly differentiates it from the sibling tools (brickognize_identify_fig, brickognize_identify_part, brickognize_identify_set) that are for specific known item types, offering clear alternatives.

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