create_aggregate_mc

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It does not mention that the tool writes a formula into a cell (target_cell), the effect of dry_run, or any side effects on the workbook, leaving significant gaps for the agent.

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 short and front-loads the core purpose, but it lacks structure and fails to cover essential details. While concise, it sacrifices completeness for brevity.

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 9 parameters with 0% schema coverage and a complex function, the description is severely incomplete. It does not explain how results are returned, the role of dry_run, or the purpose of limits and shift, leaving the agent underinformed.

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 0%, so the description must provide parameter context. It only explains frequency_object_cell and severity_object_cell, ignoring 7 other parameters like target_cell, dry_run, limits, and shift. Most parameters remain unexplained, making correct invocation difficult.

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 builds a VoseAggregateMC formula for simulating aggregate losses, naming the specific function and the roles of n and distribution. However, it does not distinguish from the similar sibling tool 'create_aggregate', leaving potential confusion about when to use which.

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 implies usage for simulation aggregate loss models but provides no guidance on when to choose this tool over alternatives like 'create_aggregate' or 'create_risk_event', nor does it mention prerequisites or limitations.

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