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

With no annotations, the description carries the full burden, and it does disclose the key async trait: text is usable immediately while a blog URL is processed in the background. However, it says nothing about permissions, whether adding a sample mutates or overwrites the author's existing voice, or any limits on sample count.

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?

Three short sentences with no filler, and the primary purpose (feeding create_article's voice) is front-loaded before the synchronous/asynchronous detail.

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 4-parameter mutation tool with no annotations and no output schema, the description covers the core input duality and downstream purpose but omits the meaning of title and author_id, what the call returns (e.g. a sample identifier), and whether existing samples are affected.

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 50%: content and blog_url are already documented in the schema (including the 100-character minimum and the omit-when-using-blog_url rule), and the description essentially restates the text-vs-URL dichotomy. The title and author_id parameters receive no semantic elaboration in either place, so baseline 3 is appropriate.

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 and resource ('Add a writing sample to an author') and names the downstream consumer and sibling, create_article, that the sample feeds. An agent can distinguish this from add_chatbot_content or create_author without opening any schema.

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 clause 'so create_article can use that voice' gives a clear reason to call this tool rather than another, establishing the author-voice use case. It stops short of stating exclusions or contrasting with the sibling add_chatbot_content, which would be needed for a 5.

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