Get Writing Preferences

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by stating the output includes feedback insights and requires an open project, which is a precondition not captured by annotations. This goes beyond what annotations convey.

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 two sentences plus a brief mention of related tools. It is front-loaded with the primary function and lists outputs, then provides usage guidance. Every sentence adds value without redundancy.

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?

Given the tool has no parameters, an output schema exists, and annotations cover safety, the description is fully sufficient. It explains what the tool returns, when to use it, a prerequisite (open project), and related tools. No gaps remain for typical usage.

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?

The tool has no parameters, so the description cannot add meaning beyond the input schema. Baseline for 0 parameters is 4, and the description appropriately focuses on the tool's purpose and outputs rather than parameters.

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 returns 'current author writing preferences plus feedback insights' and enumerates specific outputs: feedback recorded, average satisfaction, suggestions. It distinguishes from related tools by mentioning 'set_writing_preferences' and 'collect_feedback' and specifying the use case to review current AI steering or feedback trends.

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 explicitly says 'Use to review what is currently steering AI output, or to see whether recorded feedback points to a change' and notes the prerequisite 'Requires an open project.' It names related tools, giving context for when to use this tool versus alternatives, though it does not explicitly state when not to use it.

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