simulate

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

With annotations all false, the description carries the full burden and does so thoroughly. It discloses synchronous deterministic execution, rate limits and quota with specific 429-on-excess behavior, asynchronous run recording visible in list_runs, and the exact return envelope 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?

Three sentences convey action, use cases, behavioral constraints, and return value structure with no filler. Every sentence earns its place and key information is front-loaded.

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?

The description is complete for a compute-and-recommend tool: it covers when to use it, behavioral constraints, rate limiting, async persistence, and a full list of return fields despite the absence of an output schema. No critical calling information is missing.

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 description coverage is 100%, so the baseline is 3. The description does not add parameter-level detail beyond the schema, but it does not need to since the schema already documents all parameters.

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 states a specific action ('Run a Monte Carlo simulation') and a clear resource/output ('get a structured decision recommendation'). It clearly distinguishes from siblings like simulate_repository and recommend by emphasizing probability-weighted decision outcomes.

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?

Provides explicit use cases: 'quantifying risk in a decision, comparing expected outcomes, getting probability-weighted recommendations.' This gives clear context for when to use it, though it does not name alternatives or exclusion conditions.

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