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

Annotations already mark the tool as readOnlyHint=true, so no mutation is expected. The description reinforces this by stating it 'computes' and 'returns' results, consistent with a read-only calculation. It adds behavioral context beyond annotations by describing the return values (dollar stake and risk rating) and the optional nature of bankroll. No side effects, destructive actions, or auth requirements are mentioned, which is fine given the compute-only nature.

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 extremely concise: one sentence states core purpose, one lists key inputs and outputs, one provides usage examples. Every sentence contributes value. It front-loads the primary action and avoids unnecessary detail. No filler or repetition.

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 with well-documented parameters and no output schema, the description adequately covers inputs, outputs, and usage. It mentions outputs (dollar stake and risk rating) but does not define the risk rating format. Given the tool's low complexity and 100% schema coverage, this is nearly complete. A minor gap is the lack of precision about the risk rating (e.g., 'low/medium/high' or numeric), but overall it's sufficient for an agent to use correctly.

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 100%, providing baseline value. The description supplements the schema by explaining the overall purpose, linking to natural-language queries, and hinting at output format. For instance, it clarifies that omitting bankroll yields a percentage result. This adds meaningful context beyond the raw schema, especially given the lack of an output schema.

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 computes optimal Kelly position size for prediction-market contracts. It specifies the verb 'Compute', the resource 'optimal Kelly position size', and the domain 'prediction-market contract'. This distinguishes it from sibling tools like 'calculate_ev' (expected value) or 'convert_probability' (probability conversion), which address different aspects of betting decisions.

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 concrete natural-language query examples ('how much should I stake', 'what is my position size', 'Kelly sizing for this trade'), giving clear usage context. It does not explicitly state when not to use this tool or suggest alternatives, but the context of siblings implies its specialized purpose. A small improvement would be a brief exclusion note (e.g., 'not for expected value or probability conversion').

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