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

With no annotations, the description carries full behavioral disclosure. It reveals the return format (JSON { outline }), the fact it writes only a skeleton, that it is a paid model call with a token cap, rate limiting, and caching behavior. This is substantial and actionable context.

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 dense but every sentence contributes unique information: purpose, return format, scope and alternatives, cost/token cap, rate limit and caching. It is front-loaded with the core function and keeps all sentences relevant.

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 two-parameter tool with no annotations and no output schema, the description covers all necessary ground: what it does, what it returns, how it differs from siblings, and operational constraints. Nothing critical is missing for an agent to call it correctly.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining that a large section count yields thinner sections due to the token cap, which is behavioral context tied to the 'sections' parameter that the schema does not provide.

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 produces a markdown heading structure for a blog post with specific components (title, introduction, sections, conclusion, FAQ). It explicitly distinguishes itself from generate_text and humanize_text, making the resource and scope 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 guidance: use this tool for the skeleton only, and use generate_text for body prose and humanize_text for reworking existing text. This gives clear when-to-use and when-not-to-use signals relative to siblings.

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