run_monte_carlo_simulation

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only restates the tool's function and does not describe return format, computational intensity, side effects, or whether it is read-only. For a simulation tool, users would benefit from knowing it produces a distribution of outcomes or price paths, but this is absent.

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 concise sentence with no filler or redundant content. It front-loads the main action (Run Monte Carlo simulations) and provides context (exotic payoffs and risk analysis). However, it is so short that it borders on under-specification, but for pure conciseness efficiency, it earns a 4.

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 the tool's complexity (8 parameters, no output schema, no annotations), the description is inadequate. It fails to explain what the simulation returns (e.g., price estimate, confidence intervals, risk metrics), how to interpret outputs, or any caveats. This leaves significant gaps for the agent to correctly use and understand the tool's results.

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?

All 8 parameters have complete descriptions in the schema (100% coverage), so the baseline is 3. The description adds no additional semantic meaning beyond the schema fields; it merely mentions 'exotic payoffs' which is already captured by the product_type enum. Thus, the schema does the heavy lifting, and the description contributes little.

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 the tool runs Monte Carlo simulations, with a specific focus on exotic payoffs and risk analysis. This aligns with the parameter enum (autocallable, barrier, asian, lookback) and conveys a clear purpose. However, it does not differentiate from the sibling tool run_monte_carlo_robustness_test, which also involves Monte Carlo simulations.

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 alternatives such as run_monte_carlo_robustness_test or stress_test_scenarios. It does not mention exclusions, prerequisites, or appropriate use cases beyond the generic statement, leaving the agent without decision support.

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