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

Annotations already supply readOnlyHint=true and destructiveHint=false, but the description adds far richer behavioral context: cost implications (consumes AI credits regardless of usage), slower runtime, non-determinism (two identical calls give different text, consistent with idempotentHint=false), no persistence, and prose output requiring verification. No contradiction with annotations.

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 ~100-word description is dense but every sentence earns its place: purpose, usage timing, alternatives, side effects, cost, non-determinism, and output format. It is front-loaded with the core purpose and each subsequent sentence addresses a legitimate concern for a generative, costly tool. No wasted words.

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 1-parameter generative tool with no output schema, the description is complete: it explains what is returned (insights, sources, outline; prose not JSON), when to run it, what it does not do, its cost and non-determinism, and advises verifying factual claims. The absence of an output schema is compensated by the description covering return format and reliability caveats.

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% and the single 'query' parameter is already well documented in the schema, including a specificity tip. Per the rubric, high coverage sets a baseline of 3. The description adds minimal parameter-level meaning beyond what the schema provides, so the baseline stands.

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 opens with a specific verb+resource+outcome: 'Research a topic with AI and return insights, sources, and a suggested content outline.' It clearly differentiates from siblings by explicitly naming generate_title_seo (for titles) and search_articles (for existing content), making the tool's 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?

Provides explicit when-to-use guidance ('Use it at the START of a piece, before drafting') and what it is NOT for ('not a finished article and not a title'), plus named alternatives for those cases. This is exactly the level of usage direction the rubric demands.

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