Cost Check

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

Annotations already declare readOnlyHint and idempotentHint. The description adds valuable behavioral context: costs are charged at both entry and exit, always against the position, and the model matches the gym's scoring. This goes beyond the annotations and clarifies the pessimistic nature of the calculation.

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 concise sentences. The first states the core purpose, the second explains the model. No wasted words, front-loaded, and easy to parse.

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 calculation tool, the description covers the model, and the output schema is present (not shown but indicated). Annotations cover safety. There is nothing an agent needs to invoke it correctly that is missing; the cost model is fully disclosed.

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% with individual parameter descriptions, so the baseline is 3. The description adds meaning by stating that all costs (spread, slippage, adverse selection) are applied at both entry and exit, which clarifies how the parameters are used together. This is useful but not deeply per-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 states a specific verb ('Convert') and a clear resource ('a claimed gross edge into net-of-cost reality'), and it distinguishes itself from siblings by naming the 'pessimistic fill model' that the gym uses. This is unambiguous and separates it from validation or audit tools.

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 clearly implies when to use it (when you have a gross edge and want the net after costs), but it does not explicitly state when NOT to use it or name alternatives. Sibling tools like validate_order or explain_sizing have different purposes, so the context is somewhat inferable, but explicit guidance is missing.

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