Describe the simulation model

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

Annotations already declare readOnlyHint=true, but the description adds value by detailing the content of the output (assumptions and limitations). No contradictions. The behavioral context is well explained beyond the annotation.

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?

Two sentences that are front-loaded and contain all necessary information without redundancy. Every sentence serves a purpose, making it 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?

Despite no output schema, the description comprehensively lists the return content and mentions the tool's value in understanding model limitations. It is complete for a descriptive read-only tool.

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?

No parameters exist (0 parameters, schema coverage 100%). The description is not required to add parameter semantics, and it does not. Baseline score of 4 is appropriate for this case.

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 explicitly states that the tool returns modeling assumptions and lists specific components (DF measurement, bearing-error model, etc.). It clearly differentiates from sibling tools like 'run_engagement' which are for running simulations.

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 advises to read the output before drawing conclusions, providing clear when-to-use guidance. It also implies this tool is for understanding model limitations, not for running simulations.

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