QuantOracle

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

Annotations establish read-only/idempotent safety; the description adds valuable behavioral context by specifying convergence characteristics (5-8 iterations), which informs latency expectations. However, it omits failure modes or output format details.

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?

Two sentences with zero waste: first establishes identity and algorithm, second provides performance characteristics. Information is front-loaded and dense.

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?

While adequate for a calculation tool with complete input schema, the absence of an output schema creates a gap—the description should specify what value/object is returned (e.g., implied volatility as decimal, convergence metadata).

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 schema fully documents all 7 parameters. The description does not add parameter-specific semantics, meeting the baseline expectation when the schema carries the descriptive 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?

The description specifies the exact algorithm (Newton-Raphson) and domain task (implied volatility solver), clearly distinguishing it from sibling tools like options_price (forward pricing) and stats_realized-volatility (historical calculation).

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?

While domain experts can infer this is for backing out volatility from observed market prices (inverse of pricing), the description lacks explicit when-to-use guidance versus options_price or warnings about convergence requirements.

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