simulate_montecarlo

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

Annotations (readOnlyHint=true, destructiveHint=false) already indicate safety. The description adds behavioral details: it returns simulated terminal prices, percentile distribution, expected value, probability of profit, and path statistics. It also notes performance limits and API fallback, providing context beyond structured fields.

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 plus a note; no wasted words. The first sentence captures the core functionality, and the note provides essential usage constraints. Information is front-loaded and easy to parse.

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?

With 7 parameters and no output schema, the description covers purpose, usage limits, and expected outputs (terminal prices, percentiles, etc.). It lacks error handling details but is fairly complete for a simulation tool with defaults. Annotations further enrich context.

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?

Schema coverage is 100% with all 7 parameters described in the input schema. The description mentions 'starting price, drift, volatility, time horizon, number of simulations' and contributions/withdrawals, but does not add significant meaning beyond what schema descriptions already provide. Baseline 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 clearly states the tool performs GBM Monte Carlo simulation with contributions/withdrawals, up to 5000 paths, and lists returned outputs (terminal prices, percentiles, etc.). It distinguishes from sibling tools which focus on bonds, indicators, options, or risk metrics, none of which are Monte Carlo simulations for asset price paths.

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 says 'Use when running a Monte Carlo simulation for asset price paths' and provides performance constraints: keep simulations ≤ 1000 and years ≤ 30 for MCP, and directs to REST API for larger simulations. It does not mention when not to use this tool versus alternatives, but the usage context is well-defined.

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