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

Annotations already cover the read-only/open-world safety profile, yet the description adds materially new behavior: typical latency (5-8 seconds), a paid x402 per-call model, and the failure-and-no-charge contract. This is exactly the extra context annotations cannot express.

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?

One front-loaded paragraph ordered purpose, output, usage, then cost/behavior — every sentence carries information. Slightly dense, and listing the output field names is mildly redundant given an output schema exists, but nothing 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?

Covers purpose, required input wording, exclusions, latency, pricing, and failure semantics; with an output schema present, it need not explain return values. An agent has everything needed to decide and invoke correctly.

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 baseline is 3, but the description adds real guidance beyond the schema by telling the agent to include the market's date or threshold and giving concrete examples ('by Dec 31, 2026', 'above 4.5%'). That is actionable formatting advice the schema does not supply.

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 and resource ('Builds an evidence brief for a prediction-market question ... from the live web') and even enumerates the output shape, so an agent can distinguish it from generic research siblings without opening the schema.

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 routes general questions to a named alternative ('Use research.answer for general questions') and states what it is not for ('no probability, forecast or trading advice'). It stops short of contrasting with the other research.* siblings (factcheck, company), so it is clear but not exhaustive.

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