fahali_get_flash_crash_risk

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

With no annotations, the description carries full burden. It fully discloses the detection strength formula (risk_weight × alert_confidence), calibration status (false), what the tool does NOT return (P(crash), time-to-crash, magnitude), and the engine's weak performance (realized lift ~0). This is highly transparent.

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 moderately concise; every sentence adds value. It front-loads the key caveat ('NOT a calibrated crash probability'). A slight reduction in length could improve conciseness, but it is well-structured.

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?

Given no parameters, no output schema, and the tool's complexity, the description is remarkably complete. It explains the signal's nature, what it ranks, what it does not provide, and its empirical performance. An agent can fully understand the tool's capabilities and limitations.

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?

There are zero parameters, so the baseline is 4. The description adds value by explaining what the output represents, despite no input schema. No additional parameter information is needed.

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 clearly states it provides per-symbol flash-crash 'precursor detection strength' and explicitly distinguishes it from a calibrated crash probability. The verb 'get' combined with 'detection strength' makes the purpose unambiguous. It also differentiates from sibling tools by focusing on precursor signals.

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 explicitly tells when to use (to rank symbols with flashing precursor signals) and when not to use (not for calibrated probability, time-to-crash, or move magnitude). It even mentions that realized lift is ~0 and actionability is withheld, guiding the agent to avoid over-reliance. Also states 'Public data — no tier required', clarifying access.

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