ai-furniture-hub

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs checks on multiple technical aspects (llms.txt, robots.txt, structured data, etc.), returns a 0-100 score and A-F grade, and evaluates cross-border readiness. However, it does not mention potential side effects, rate limits, authentication needs, or error handling, leaving some behavioral gaps.

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, starting with the core functionality and then detailing the checks and outputs. However, the last sentence about the demo use case, while relevant, could be considered slightly extraneous, preventing a perfect score for conciseness.

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 (diagnosing AI visibility with multiple checks) and the absence of annotations and output schema, the description does a good job of explaining what the tool does, what it checks, and what it returns (score and grade). It could be more complete by detailing the output structure or error cases, but it provides sufficient context for an agent to understand the tool's purpose and use.

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 both parameters ('intent' and 'url'). The description adds context by explaining that the URL is for the site to diagnose, but it does not provide additional meaning beyond what the schema states (e.g., examples of valid intents or URL formats). Baseline 3 is appropriate when the schema handles parameter documentation adequately.

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: 'diagnose how well a site is visible to AI agents' by checking specific technical aspects (llms.txt, robots.txt, structured data, etc.) and returning a score/grade. It specifies the verb ('diagnose'), resource ('site'), and scope ('AI agents like GPT/Claude/Gemini'), distinguishing it from sibling tools that focus on product search, comparison, or layout calculation.

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 implies usage context ('URLを指定すると' - when you specify a URL) and mentions a demo use case ('AIOエージェンシーのデモとして'), but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No sibling tools appear to offer similar functionality, so the lack of explicit alternatives is understandable, but guidance remains implicit.

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