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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so safety behavior is covered. The description adds important behavioral context: it is a paid Pro tool, priced per call, with Credits for signed-in users and x402 USDC payment for anonymous callers.

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?

The description is front-loaded with the core purpose and then explains payment/auth handling before the argument. The payment details are somewhat verbose but relevant to correct invocation, and the overall structure is easy to scan.

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 one documented parameter, rich annotations, and an output schema, the description covers the main gaps an agent needs to call the tool correctly. The main missing element is sibling differentiation, which is more of a usage-guideline gap than a completeness gap.

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 0%, so the description must carry the parameter meaning. It does so adequately for the single parameter by stating 'symbol: Stock symbol, e.g. "NVDA"', giving both semantic meaning and an example.

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?

The description states a specific resource and output: a full pre-trade risk scan JSON for a stock. It is clear what the tool does, but it does not distinguish itself from siblings such as analyze_stock, get_monte_carlo, or get_ai_prediction.

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?

The description gives practical invocation guidance around payment and authentication modes, and 'pre-trade risk scan' implies a use context. However, it never states when to choose this tool over sibling analysis tools or when not to use it.

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