expert_onboarding_research

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

Annotations declare the non-read-only, non-idempotent, non-destructive profile, so the safety burden is partly covered. The description adds genuinely useful behavior beyond that: the job runs asynchronously for 1-2 minutes and the caller must not block on it. It does not say how or when results become available.

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?

Three short sentences, front-loaded with the action and scope, then the async timing, then the immediate next step. No filler; every sentence changes agent behavior.

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 fire-and-forget async trigger with no output schema and a single fully documented parameter, the description covers everything needed to invoke it correctly and what to do next. The one omission is how the agent later retrieves the research results, which a polling sibling likely handles.

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% for the single optional 'userId' parameter, so the schema already carries the semantics. The description adds nothing about the parameter, which is acceptable when coverage is complete but earns only the baseline.

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?

States a specific verb ('Initiate background research') and a specific resource (the expert's public work and domain insights), plus the downstream purpose (expertise and voice modeling). It is distinguishable from siblings like deep_research_topic or check_research_status, though it does not name them explicitly.

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?

Gives clear usage context: run this, do not wait, proceed to the Tone of Voice study in chat. The 'do NOT wait' instruction removes ambiguity about blocking behavior. It stops short of naming the sibling used to poll results (e.g., check_research_status).

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