aetre_simulate_benchmark

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool computes intervals and clarifies they are run-to-run rather than confidence intervals, which is a positive nuance. However, it fails to disclose whether the tool has side effects (e.g., mutates state), what authentication is required beyond the api_key parameter, or any rate or resource limits. The lack of output description is a significant gap.

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 a single, dense sentence that front-loads the core action and then specifies metrics and statistical detail. There is no redundant or filler text. While it is information-dense, it is not overly long and every clause adds meaning, earning a solid 4.

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 tool's complexity (8 parameters, Monte Carlo simulation) and the absence of an output schema and annotations, the description should explain what the tool returns. It does not mention the result format, whether it is a table, a summary, or a detailed breakdown. It also does not discuss any prerequisites or consequential requirements beyond the api_key parameter. This is a major omission for an agent to use the tool effectively.

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% per the context, so all 8 parameters have descriptive schema entries (e.g., 'Number of Monte Carlo simulation replicates (default: 50)'). The description does not add any parameter-specific details beyond what the schema already provides; it only references the simulation design generally. Thus it stays at the baseline of 3.

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 a specific verb ('Runs') on a defined resource ('paired-cohort Monte Carlo simulation') and enumerates the exact metrics compared (Quality Throughput, FDR, Unconventional Recall, Human Reviews) across all 4 screening regimes. It also adds a precision detail about the intervals, which distinguishes it from generic simulation descriptions. It does not explicitly name a sibling tool, but the specificity is strong enough to convey distinct purpose.

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?

There is no guidance on when to use this tool versus the many sibling tools such as aetre_heterogeneous_queues or aetre_author_preflight_benchmark. The description does not mention any exclusions, prerequisites, or alternative conditions. The agent must infer the intended use case from the name and description alone, which is insufficient for such a specialized simulation tool.

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