QuantOracle

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

Annotations already establish read-only, non-destructive, idempotent characteristics. The description adds valuable context about the model (GBM) and operational constraints (5000 path maximum). However, it fails to disclose what the tool returns (raw paths vs. percentiles vs. summary statistics), random seed behavior, or computational complexity, which is critical information given the lack of output schema.

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 consists of two highly efficient sentences with zero filler. It front-loads the essential model identifier (GBM) and ends with the critical constraint (5000 paths). Every word earns its place.

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 7 parameters with full schema coverage and helpful annotations, the description meets minimum viability. However, for a simulation tool lacking an output schema, it should describe the return value structure (e.g., 'returns array of simulated portfolio paths' or 'returns percentile analysis') to be complete.

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 baseline score is 3. The description mentions 'contributions/withdrawals' which aligns with the parameters, but adds no additional semantic context about parameter interactions (e.g., that contributions and withdrawal_rate apply simultaneously) or format details beyond what the schema already provides.

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 mathematical model (GBM - Geometric Brownian Motion), the method (Monte Carlo), and distinct features (contributions/withdrawals, 5000 path limit). It effectively distinguishes this from sibling analytical tools like stats_sharpe-ratio or tvm_cagr by indicating it's a stochastic simulation. It loses one point for not explicitly stating the domain (portfolio/financial asset simulation) though this is inferable from parameters.

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 provides no guidance on when to use this tool versus deterministic alternatives like risk_portfolio or tvm_cagr. It does not mention prerequisites, appropriate use cases (e.g., retirement planning vs. short-term forecasting), or when the stochastic approach is necessary versus deterministic calculations.

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