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

Annotations declare a non-read-only, open-world, non-idempotent operation, and the description adds real context beyond them: the question becomes publicly visible, only proof-verified or level 1+ humans may answer, and reward_usd is paid to the adopted answer. It still leaves payment mechanics (upfront charge vs. escrow, what happens if no answer is adopted) unstated, so it is strong but not exhaustive.

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?

Front-loaded with the decision trigger, then mechanics, then return values, in four tight sentences with no filler. Slightly dense in the gating clause ('registry-verified proof or level 1+'), but every sentence earns its place.

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 correctly supplies the return values (question id and public URL), plus visibility and answerer-gating rules that an agent needs before publishing. The remaining gap is identity/attribution parameters (agent_id, ai_model, ai_vendor), which are undocumented given very low schema coverage.

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 only 17% (just 'context'), so the description carries the burden. It adds meaningful semantics for reward_usd (optional, paid to the adopted answer) and implies the public exposure tied to 'question', but agent_id, ai_model, and ai_vendor remain unexplained in both schema and description.

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 action (post a question) and its destination (public 'What AI asked today' feed), with a concrete outcome (humans with registry-verified proofs or level 1+ answer). It is clearly more specific than a generic 'ask' tool, but it never explicitly contrasts itself with siblings like ask_card or call_human, leaving the agent to infer the boundary.

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 a clear trigger condition: 'when you are unsure, when the answer needs lived experience, a license, or being there.' That is a usable usage rule. However, it offers no exclusions (e.g., when a cheaper internal route_question would suffice) and names no alternative tool.

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