ai-furniture-hub

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it uses Vision AI to extract features, queries catalog+Rakuten, and returns detailed info (model numbers, internal dimensions, consumables, compatible boxes). However, it lacks details on permissions, rate limits, error handling, or response format. The description adds useful context but doesn't fully compensate for the absence of annotations.

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 appropriately sized and front-loaded: it starts with usage scenarios, explains the process (Vision AI → features → catalog+Rakuten query), and ends with output details. Every sentence adds value, though it could be slightly more streamlined (e.g., by merging some clauses). No wasted words, but minor redundancy exists.

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 complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, usage, and high-level behavior but lacks details on output structure, error cases, or advanced usage. For an identification tool with rich parameters and no output schema, it should ideally explain return values or limitations more explicitly.

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 already documents all parameters thoroughly. The description mentions 'features' and implies 'intent' through usage examples but doesn't add significant semantic value beyond what the schema provides. It doesn't explain parameter interactions or provide additional syntax/format details. Baseline 3 is appropriate when the schema does the heavy lifting.

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 tool's purpose: identifying products from photo/feature text using Vision AI, returning candidates from catalog+Rakuten with model numbers, internal dimensions, consumables, and compatible box information. It specifies the verb ('identify'), resource ('products'), and scope (photo/feature text → product identification with detailed info), distinguishing it from siblings like search_products or get_product_detail which lack this specific identification functionality.

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 explicitly states when to use this tool: for queries like 'What is this shelf in the photo?' or 'I want to know boxes that fit my shelf.' It provides clear context (photo/feature text scenarios) and implicitly distinguishes from siblings by focusing on identification from visual/textual features rather than general searching, comparison, or detail retrieval.

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