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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description is free to add genuinely new context: the 1-5 minute latency, the asynchronous job_id pattern, and the required polling via get_deep_answer. It also discloses what verification is done against (on-chain receipts, ledger, issue tracker, deployed code) and what evidence the answer carries.

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?

Dense but front-loaded: the verification standard, async pattern, and routing guidance all appear in the first pass. The parenthetical about api_key/register_agent is slightly awkwardly placed up front, but no sentence is wasted.

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 steps in to explain the return value (a job_id) and the exact tool to poll for the result. Combined with latency, verification sources, and routing guidance, nothing an agent needs to invoke and follow up correctly is missing.

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%, so both parameters are already documented. The description restates the api_key requirement and its provenance (register_agent), which mirrors the schema rather than adding syntax or constraint detail. Baseline 3 is appropriate when the schema carries the parameter burden.

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 (Ask) and resource (EventTrader's AI, NEXUS) and defines the scope tightly: the platform's highest evidence standard with per-claim verification. It distinguishes itself from the sibling ask_nexus by framing itself as the deep/verified investigation versus the ordinary quick-read tools.

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?

Explicitly lists when to use it (support, verification, 'is this really true/fixed/delivered') and names the alternative for the opposite case ('use the ordinary tools for quick data reads'). It also names the required follow-up tool, get_deep_answer, for polling.

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