avg_grade_level

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

No annotations provided, so description carries full burden. It mentions 'across multiple readability formulas' but does not disclose error handling, edge cases (e.g., empty text), or range of output. With no annotations, more behavioral detail is needed.

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 extremely concise with no wasted words. It front-loads the purpose and immediately lists the parameter. For a simple tool, this is appropriate.

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 simplicity and presence of an output schema (not shown), the description could be minimally complete. However, it lacks any mention of return value or behavior. It is adequate but not comprehensive.

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 description must add meaning. It explains 'text' as the input for calculation, which clarifies its role. However, it does not specify constraints like minimum length or encoding, so it adds limited value beyond the schema.

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 it calculates average grade level across multiple readability formulas, with a specific verb and resource. It distinguishes from sibling tools that calculate single readability scores (e.g., ari_score, fry_readability). However, it does not specify which formulas are included.

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?

No guidance on when to use this tool versus other readability tools. The description does not mention that it is an aggregate or provide context for selection among siblings like analyze_readability, gunning_fog_score, etc.

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