content_quality

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states what the tool evaluates and that it returns improvement suggestions, which signals a read-only analysis. However, it does not mention any constraints (e.g., public URL requirement, JS-heavy page limitations) or explicitly confirm an absence of side effects, so some transparency is missing.

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 brief and front-loaded with the core purpose, followed directly by the evaluation criteria. There is no redundant or filler content; each sentence earns its place.

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?

Though the tool has only one parameter and an output schema supplies the return value shape, the description provides the key evaluative dimensions. It does not mention network prerequisites or usage boundaries, but these are not essential for a simple analysis tool. Overall, an agent can invoke it correctly with just a URL.

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?

The schema has a single 'url' property with zero description coverage, so the description must compensate. It does so by naming the resource as a 'web page,' making the parameter's purpose clear. However, it does not specify URL format or expected scope, relying on common sense. This is sufficient for a simple parameter but not a robust semantic addition.

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 uses a specific verb ('Analyze') and resource ('content quality of a web page') and lists concrete evaluation dimensions (Flesch-Kincaid, Gunning Fog, keyword density, content structure, metadata quality). This distinguishes it from siblings like metadata_extract and web_extract, which focus on extraction rather than quality scoring.

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 clearly implies this tool is for content quality auditing of a web page, but it does not explicitly state when to choose it over siblings such as metadata_extract or web_extract, nor does it provide when-not-to-use conditions. It relies on the agent to infer usage context.

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