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

The description adds substantial behavioral context beyond annotations: queuing ('lands in their Hunchful inbox'), default emailing, deferred mutation ('nothing is added until they answer'), and auth via OAuth or contributionToken. This enriches the annotation set (readOnlyHint: false, openWorldHint: true) without contradiction.

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 compact and front-loaded with the core purpose. Every sentence adds distinct information: purpose, behavior, auth, and usage frequency. No redundant or vague phrasing.

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 an output schema present and high schema parameter coverage, the description provides the key missing context: the queued/email side-effect, auth requirements, naming guidance, and usage restrictions. It is fully adequate for an 8-parameter tool with moderate complexity.

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 high (88%), so the baseline is 3. The description adds value by providing a naming convention for 'name' ('situational axes welcome: name it 'When <situation>'') and clarifying that auth is via OAuth (modelId) or contributionToken, which helps interpret those 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 states the exact purpose: 'Suggest a NEW question/axis for the person — a dimension their model doesn't capture'. The verb ('Suggest'), resource ('question/axis'), and scope ('their model doesn't capture') are specific and distinguish it from siblings like add_hunch or propose_refinement.

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 provides clear context: 'It lands in their Hunchful inbox... nothing is added until they answer' explains the flow, and 'Use sparingly — about once a week at most' gives rate guidance. It also mentions auth prerequisites. However, it doesn't explicitly name sibling alternatives, relying on the 'NEW' framing to differentiate.

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