Identify LEGO Set

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

Annotations cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds value by specifying acceptable image sources (box, assembled model, instructions) and the return format (matched sets with numbers, names, confidence scores, links), which are not covered by annotations. It does not contradict annotations, as 'identify' aligns with read-only operations.

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 in the first sentence, followed by usage guidance and parameter/return details. Every sentence earns its place by providing essential information without redundancy, making it highly efficient and well-structured.

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 (2 parameters, 100% schema coverage, annotations provided, no output schema), the description is mostly complete. It covers purpose, usage, parameters, and returns, but lacks details on error handling or limitations (e.g., image size constraints). With annotations handling safety and idempotency, it's sufficient but not exhaustive.

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 baseline is 3. The description adds minimal semantics beyond the schema: it mentions 'imagePath' and repeats the file format (JPEG, PNG, or WebP), which is already in the schema, and does not elaborate on 'includeRaw'. Thus, it meets the baseline without significant added value.

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 specific action ('Identify a LEGO set from a photograph') and resource ('LEGO set'), distinguishing it from siblings like 'brickognize_identify_fig' or 'brickognize_identify_part' by specifying the target is a set. It explicitly mentions acceptable image types (box, assembled model, or instructions), making the purpose highly specific and differentiated.

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 vs. alternatives: 'Use instead of brickognize_identify when you know the image shows a LEGO set.' This directly addresses sibling tools and clarifies the context for selection, offering clear alternatives and exclusions.

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