score_readability

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

With no annotations, the description must fully disclose behavior. It states the metrics and flagging but omits critical details: no mention of return format, error conditions, or the role of the optional api_key parameter. The recommendation of minimum 50 words hints at behavior but is insufficient.

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 concise, using two sentences and a bullet-style args list. It is front-loaded with the core function. However, it could be slightly more structured (e.g., separate behavior and return sections), but overall it's efficient with minimal waste.

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 has no output schema, no annotations, and moderate complexity (two parameters, multiple metrics), the description falls short. It does not specify return values, which are needed for the agent to use the output, nor does it explain when to provide api_key. The context is incomplete for safe and correct invocation.

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 0%, so the description must compensate. It only explains the 'text' parameter with a usage hint (minimum ~50 words), but entirely ignores the 'api_key' parameter. This leaves one parameter undocumented, failing to add meaning beyond the schema's bare types.

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 calculates readability metrics, listing four specific indices (Flesch Reading Ease, etc.) and additional flagging of long sentences and filler words. The verb 'Calculate' and resource 'readability metrics for text' are specific, and the tool is well-differentiated from sibling tools (tone, outline, similarity, headlines).

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 when readability scores are needed, but provides no explicit guidance on when not to use this tool or mention alternatives. It does not leverage sibling tool names to clarify boundaries, leaving the agent without context for selection.

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