aetre_evaluate_staking

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

No annotations are provided, so the description must convey behavioral traits. It says 'Simulates,' implying a non-mutating computation, but it does not explicitly state that it has no side effects, requires an API key (though listed in schema), or what happens if inputs are invalid. It fails to disclose whether it is read-only or if any external data is accessed.

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 sentence without fluff, delivering the core purpose efficiently. It is appropriately front-loaded with the primary action and context, though it is slightly dense and could be broken into clearer clauses. Overall, it is concise and structured acceptably.

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?

With no output schema and no annotations, the description leaves critical gaps. It does not describe the result format (e.g., equilibrium number of submitters, threshold conditions), any assumptions or edge cases (e.g., when N < K), or the need for the api_key parameter. An agent cannot fully anticipate the tool's behavior or output from this description alone.

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 description coverage is 100%, so all parameters are already explained (e.g., generation_cost, submission_fee). The description adds only contextual phrasing like 'generative AI generation costs' and 'refundable submission deposits,' which map directly to those parameters without adding new semantic detail beyond the schema. This meets the baseline for high coverage with no extra compensation needed.

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 states a specific verb ('Simulates') and object ('submitter entry equilibrium') with context (generative AI generation costs, refundable submission deposits). It is clear on what the tool does, but it does not explicitly distinguish itself from sibling simulation tools like aetre_quadratic_staking or aetre_heterogeneous_queues, which may also model related equilibria.

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 provides no explicit guidance on when to use this tool versus alternatives. It does not mention conditions like 'use when modeling spam floods with deposits' or compare to siblings such as aetre_quadratic_staking. The only cue is implied from the tool name and description, leaving the agent to infer its appropriate use.

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