get_simulation_results

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

No annotations are provided, so the description carries full burden. It discloses that it is a read operation, lists return values, and states a prerequisite. It does not cover error conditions or behavior when simulation has not been run.

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, front-loaded with purpose and return values, then prerequisite and filter option. No unnecessary words.

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 presence of an output schema, the description covers the key points: what it does, what it returns (list of statistics), prerequisite, and filtering. Minor gap: no mention of behavior if simulation not run or if no outputs match.

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 add meaning. It explains 'output_names' as a filter but does not detail its format or the role of 'workbook_name'. Partial compensation for one parameter.

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 it reads simulation result statistics, listing specific metrics (mean, stdev, etc.), and distinguishes from siblings like 'get_samples' or 'get_tail_risk' by focusing on summary statistics per output.

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?

It provides clear context: requires a simulation to have been run and allows filtering by output_names. However, it does not explicitly contrast with sibling tools like 'get_tail_risk' for when to use alternatives.

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