simulate_scenario

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that the default outcome metric is the sum of input variables and that scenarios vary individual drivers. This goes beyond annotations, though it could mention output structure.

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, front-loaded with the main action, then use cases, then additional detail and alternative. Every sentence earns its place.

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 schema coverage is 100% and output schema exists, the description covers purpose, usage, default behavior, and alternatives. It feels complete for this tool's complexity.

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 baseline is 3. The description restates that scenarios override baseCase variables, which is already in the schema. It does not add significant new semantics beyond overall context.

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?

Explicitly states it compares named what-if scenarios against a base case, returning per-scenario outcome delta and sensitivity ranking. The verb 'compare' and resource 'scenarios' are specific, and it distinguishes from simulate_montecarlo.

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: budget sensitivity analysis, deal what-ifs, capacity planning. Also gives a clear alternative: 'For random sampling from a distribution, use simulate_montecarlo.'

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