readability

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 transparently lists all expected return values (scores, counts, reading time, tips), giving the agent a clear picture of what the tool produces. However, it does not discuss potential edge cases, limitations, or any side effects, which would elevate it further.

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 efficiently structured: the first sentence concisely states the core function and outputs, and the second provides actionable usage guidance. No sentences are redundant or extraneous, making it both concise and impactful.

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?

Despite having no output schema, the description enumerates the full set of returned metrics, which is crucial for an agent to understand the tool's utility. It also supplies usage context, making the description complete for a simple single-parameter read-only tool. All necessary aspects are covered.

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 input schema already provides 100% coverage of the single parameter 'text' with a clear description ('at least 10 words'). The tool description adds no additional parameter-specific guidance beyond what the schema offers, so it meets the baseline for high schema coverage without enriching parameter semantics further.

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 function with a specific verb ('Score') and resource ('text readability'), and immediately distinguishes it from sibling tools like meta_tags or keyword_density by focusing on Flesch-Kincaid metrics. It also lists concrete output elements, making the purpose unmistakable.

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 usage context: 'Use to ensure content matches your target audience.' This gives a clear scenario for when to deploy the tool, though it does not mention alternative tools or exclusion cases, so it falls short of a perfect score for guidelines.

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