list_simulations

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

Annotations already declare readOnlyHint=true, so the read-only nature is established. The description adds useful behavioral context: each entry is a compute_betas invocation with simulation_batch_id, date, and status, and clarifies the scope to 'Moment model group.' It also mentions the exclusion of Flow models. This goes beyond the baseline set by 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 three concise sentences, each earning its place: purpose, contents, and usage guidance with alternative. No redundant or filler text.

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 simple list tool with one parameter and an output schema, the description is complete. It covers purpose, entry content, usage context, and an exclusion, leaving no significant gaps.

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?

The schema covers 100% of parameters: model_group_id with a description 'UUID of the model group.' The description repeats the 'Moment model group' scope but adds no new parameter-level detail. Baseline 3 is appropriate given full schema coverage.

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's function: 'List past beta computation runs for a Moment model group.' It uses a specific verb ('List') and resource ('beta computation runs'), and explicitly distinguishes from sibling tools by stating 'NOT for Flow models — use list_flow_scenarios for those.'

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 explicit usage guidance: 'Use this to find older simulation_batch_ids for compute_returns.' It also gives a clear alternative for Flow models, naming list_flow_scenarios, which fully addresses when to use this tool versus alternatives.

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