QuantOracle

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

Annotations already declare the operation is read-only and idempotent. The description adds valuable context about the computational output—the '10 Greeks (delta through color)'—which annotations do not cover. It does not describe error conditions (e.g., handling of extreme volatility), but covers the key behavioral trait of what gets calculated.

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 a single, dense sentence with zero waste. It front-loads the model name and key differentiator (10 Greeks), making it immediately scannable.

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?

Despite lacking an output schema, the description partially compensates by specifying that 10 Greeks are returned (hinting at the output structure). For a standard financial model with well-understood inputs, this is sufficient, though explicit return type documentation would improve it.

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?

With 100% schema description coverage, the structured fields already document all parameters (S, K, T, r, sigma, q, type) completely. The description adds no parameter-specific guidance, but given the high schema coverage, the baseline score of 3 is appropriate.

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 provides a specific verb ('pricing'), identifies the exact model ('Black-Scholes'), and specifies the secondary outputs ('10 Greeks'). This clearly distinguishes it from siblings like options_implied-vol (which calculates volatility from price) and options_strategy (which analyzes multi-leg positions).

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 states what the tool does but provides no guidance on when to use it versus alternatives. It does not clarify, for example, that options_implied-vol is the inverse operation (calculating sigma from market price) or when to prefer this over options_strategy for risk analysis.

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