analyze_ai_writing

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

Annotations already declare readOnlyHint=true, and the description does not contradict this. It adds valuable behavioral context beyond the annotation: 'Runs fully offline; the text never leaves the machine' and the caveat 'This is a signal, not proof of AI authorship.' It also outlines the return structure, which is helpful given no output schema.

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 three sentences, each earning its place: first defines the analysis scope, second details the rich output structure, third includes offline privacy and an interpretive caveat. It is front-loaded with the primary action and avoids redundancy, making it highly 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?

With no output schema, the description thoroughly covers return values (score, verdict, counts, statistics, findings with fixes), languages, offline behavior, and limitations ('signal, not proof'). For a two-parameter tool, this is complete and leaves no significant gaps for an agent to invoke 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 minimal parameter meaning beyond the schema: it mentions English & Spanish, aligning with the language parameter, but does not provide additional syntax, defaults, or constraints beyond what the schema already states. The purpose context is helpful but does not materially enrich parameter understanding.

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 a specific verb ('analyzes'), resource ('text'), and scope ('stylometric tells of AI writing'), enumerating the categories and outputs. It distinguishes itself from siblings by focusing on AI-writing detection and returning a 0-100 score, while sibling tools like 'measure_predictability' and 'check_originality' target different aspects.

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 implies when to use the tool by detailing its capabilities (e.g., analyzing text for AI tells, offline privacy), but it does not explicitly state when to use it versus alternatives or mention any exclusions. Users can infer usage context but receive no direct guidance on selecting this tool over siblings.

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