simulate_scenario

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

With no annotations provided, the description must fully disclose behavior. It states the simulation returns 'a narrative + distribution' but does not clarify if the tool is read-only, whether it has side effects, or any computational or rate-limit considerations. Basic transparency is present but incomplete for a Monte Carlo tool that could be resource-intensive.

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 two sentences, front-loaded with the core action ('Run a what-if Monte Carlo') and outcome ('Returns a narrative + distribution'). Every phrase adds value, with no repetition or fluff. Highly efficient.

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 simulation tool with no output schema, the description vaguely states 'narrative + distribution' but does not specify the shape or format of the output. However, given the tool's purpose and the presence of well-documented parameters, the description is nearly complete. A more detailed output specification would improve it.

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%, so each parameter already has a description. The description adds context by grouping parameters as 'assumptions about sandwich frequency, average loss, and deductible,' but does not provide additional syntax or format details beyond the schema defaults. Baseline score of 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 uses specific verbs ('Run a what-if Monte Carlo') and clearly states the resource ('model expected premium vs expected refund/loss'). It distinguishes this simulation tool from sibling tools like 'quote_mev_cover' or 'check_swap' by explicitly naming the analysis type (Monte Carlo) and the inputs (sandwich frequency, average loss, deductible).

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 when to use (for 'what-if' modeling) but provides no explicit guidance on when not to use or alternatives. It does not mention prerequisites or contrast with sibling tools, leaving the agent to infer usage context without clear boundaries.

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