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

With no annotations, the description carries full burden for behavioral disclosure. It describes the tool as running a simulation but does not mention potential side effects, authorization requirements, computational cost, or data retention. This lack of detail limits the agent's ability to anticipate consequences.

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 with no wasted words. The purpose is stated first, followed by a bulleted list of use cases. The structure is efficient and front-loaded for quick understanding.

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?

For a 5-parameter simulation tool with no output schema, the description lacks detail on the format of the 'structured decision recommendation.' It also omits prerequisites, computational limits, or examples. While adequate, it leaves important gaps for the agent to infer.

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%, giving a baseline of 3. The description adds value by clarifying that variables represent triangular distributions, explaining the mode enum (auto vs expert), and linking objective to mode. This context goes beyond the schema's field descriptions.

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?

Description clearly states the tool runs a Monte Carlo simulation and provides a structured decision recommendation. It specifies the resource (simulation) and verb (run/get), and distinguishes from sibling simulation tools like simulate_repository and simulate_repository_patch by focusing on general decision support.

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?

Explicitly lists three use cases: quantifying risk, comparing expected outcomes, getting probability-weighted recommendations. This provides clear guidance on when to use the tool. However, it does not mention when not to use it or compare to alternatives like recommend or score.

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