simulate

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

Annotations already indicate idempotentHint=true and readOnlyHint=false. The description adds valuable context: the mandatory pysd dependency and that integration always uses Euler regardless of the model's configured method, which affects result interpretation. No contradiction with annotations.

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 compact at three sentences, front-loaded with the primary action. Each sentence adds unique information—purpose, dependency, and integration method—with no redundancy or filler.

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 the comprehensive input schema and existence of an output schema, the description covers the essential behavioral aspects: what it does, output format, dependency, and numerical method. It does not explain default variable selection or csv file side effects, but those are inferable from the schema, making this slightly above baseline.

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 all six parameters are well-documented in the schema. The description adds no parameter-level semantics beyond the schema, so the 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 clearly states the tool runs the model and returns downsampled time series with per-variable summaries (initial/final/min/max). This is a specific verb+resource+output description, but it does not explicitly differentiate from siblings like sensitivity_analysis or compare_scenarios.

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?

No explicit guidance is given on when to use simulate versus alternatives. The description implies it is the basic model run tool but lacks direct instructions on selection criteria or exclusions.

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