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

With no annotations supplied, the description carries the full burden and does disclose material behavior: the 5,000-character input ceiling, the underlying model (Llama 4 Scout on Workers AI), and a paid access requirement of $0.003 USDC on Base via x402. The payment detail is exactly the kind of precondition an agent must know before calling. What is missing is failure behavior (what happens if text exceeds the limit) and latency expectations.

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 dense sentence covering output shape, input limit, model, and price — front-loaded with what the caller gets back. Nothing is padded, though the length enum restatement is redundant against the schema and could be cut. Structure is efficient for a two-parameter utility.

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 output schema, the description does the important work of describing the return payload (summary plus key points plus language) so the agent knows what comes back. Combined with the character limit and pricing, an agent can call this correctly without further guessing. The only shortfall is the absence of routing guidance against the other ai_* tools.

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 100%, so both parameters including the enum values and the default are already fully documented in the schema. The description's parenthetical '(length short, medium or long)' merely restates the schema and adds no new meaning such as token budgets or when to prefer one length. Baseline 3 is appropriate where the schema does all the work.

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 names a concrete operation on a specific resource and enumerates the exact outputs: a one-sentence/2-3 sentence/paragraph summary, 3-5 key points, and the detected language. That is far more specific than a tautology. It stops short of 5 because it never positions itself against the obvious siblings (ai_rewrite, ai_keywords, ai_chat), leaving the agent to infer the boundary from the name alone.

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?

Usage is only implied by the input constraint (text up to 5,000 characters); the description never states when to pick this over ai_chat or ai_rewrite, nor when summarization is inappropriate. There are no exclusions, prerequisites, or fallback routing. Adequate but with a clear gap for a tool sitting in a dense family of ai_* siblings.

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