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

Annotations already establish that this is a read-only, non-destructive, closed-world computation. Beyond that, the description explains the method's purpose (correcting naive annualization for autocorrelation and adjusting PSR for skewness/kurtosis) and enumerates the returned fields, which is exactly the behavioral context needed since no output schema exists.

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 front-loaded with the tool's purpose, then usage, then returns. It is information-dense and every part is relevant, though the opening sentence is long and could be split for easier scanning.

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, the description lists the exact return fields (sharpe_period, sharpe_annualized_naive, sharpe_annualized_lo, autocorrelation_lag1, psr, skewness, kurtosis). Together with annotations covering safety and the schema covering inputs, an agent has everything needed to select and invoke the tool 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%, so the schema already documents all three parameters. The description refers to periods_per_year in the formula and to a benchmark for PSR, but adds no syntax, format, or constraint details beyond what the schema provides, making the baseline of 3 appropriate.

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 names the specific computation (Sharpe ratio with Lo 2002 annualization correction and Probabilistic Sharpe Ratio), so the agent knows exactly what resource is produced. It does not explicitly differentiate from closely related siblings such as run_dsr (Deflated Sharpe Ratio), so it falls short of a 5.

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 gives two explicit user-question triggers: asking for the real annualized Sharpe and asking how confident one can be in the Sharpe. There is no statement of when NOT to use it or which sibling tool to prefer, so no exclusions are provided.

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