get_onboarding_status

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

With no annotations, the description carries the full behavioral burden. It discloses that the tool polls background analysis + theme generation, describes output granularity per detail level, and advises polling frequency. It does not explicitly state whether the operation is read-only or has side effects, but 'Polls' strongly implies a non-mutating status check, and the completion guidance adds actionable behavioral context.

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 sentences, front-loaded with the purpose, then detail semantics, then operational guidance. No filler. Each sentence earns its place and the structure flows logically from what/why to how/actions.

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 simple polling tool with optional parameters and no output schema, the description adequately covers invocation, detail-level variations, polling rate, and post-completion action. It doesn't describe error states or exact response shape for 'full', but the provided field-level detail (audienceSummary, themeCount) gives enough context for an agent to invoke and interpret results.

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% with both parameters described, but the tool description adds critical meaning by explaining what each detail enum value returns ('short' = status only, 'medium' = adds audienceSummary+themeCount, 'full' = raw data). This goes beyond the generic schema description of 'detail' and helps the agent select the appropriate verbosity.

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?

Description clearly states the tool's function: 'STEP 3 of brand onboarding. Polls background analysis + theme generation.' It uses a specific verb ('Polls') and resource (onboarding status), and distinguishes from status tools for other workflows (e.g., get_blog_status, get_vibe_edit_status) by targeting this onboarding step.

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 clear context: it is Step 3, to be polled, and gives a concrete polling interval ('every ~15s'). It also tells the agent what to do when done ('surface the audience review + themes to the user'). It lacks explicit mention of alternatives or when not to use it, but the specificity of 'STEP 3 of brand onboarding' makes the usage context unambiguous.

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