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

With no annotations, the description carries the full burden. It discloses what is computed (score and grade level) but says nothing about input constraints, language assumptions, length limits, error behavior, or what the numeric scale means.

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?

A single tight sentence with no filler, front-loaded with the tool's function. However, the brevity comes at the cost of completeness rather than being purely efficient.

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?

For a simple single-input, stateless calculation tool with no output schema, naming the two outputs is adequate. It leaves minor gaps around language/input handling that a modest addition would close.

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?

One parameter with 0% schema description coverage, and the description adds nothing about the 'text' argument — not its expected language, size, or format. Since the schema is silent, the description should have compensated but does not.

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?

States a specific verb ('Calculate'), resource ('readability score'), and names the exact metric ('Flesch-Kincaid') plus the grade level output. This clearly separates it from siblings like word_count, proofread_text, and analyze_sentiment, though it doesn't explicitly name a contrasting sibling.

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 choose this over alternatives such as proofread_text or word_count, no stated prerequisites, and no note on text language or length expectations. The agent must infer usage entirely.

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