evt_tail_risk

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

No annotations are provided, so the description must fully disclose behavioral traits. It describes the statistical method but does not mention whether the tool is read-only, computationally intensive, modifies state, or requires specific permissions. The agent lacks information about side effects or prerequisites.

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, well-structured sentence with no extraneous words. It front-loads the core purpose and method, making it 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?

The description explains the method and outputs (EVT VaR/ES) but does not describe the return format or structure. Given the tool is a statistical analysis with three parameters and no output schema, more detail on the output (e.g., vector, data frame) would improve completeness.

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 0%, so the description must add meaning. It explains that 'threshold_q' is the quantile threshold for fitting and 'alpha' is the confidence level for VaR/ES. However, it does not describe the 'returns' parameter or provide formatting details beyond defaults. The description adds some context but is incomplete.

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 performs 'Extreme-value tail analysis' using Generalized Pareto distribution and reports EVT VaR/ES, specifying the method and outputs. It implies differentiation from siblings by noting suitability for 99.5%+ confidence levels, but does not explicitly distinguish from sibling tools like compute_var_es.

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 explicit guidance: 'use for 99.5%+ where empirical quantiles run out of data'. It gives a clear context for when to apply this tool, but does not mention when not to use it or suggest alternatives.

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