eur_distribution

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

Despite no annotations, the description fully discloses the tool's behavior: it takes inputs, fits a distribution, and returns percentiles, parameters, and KS statistic. No side effects or hidden behaviors are apparent.

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 concise with two short paragraphs: the first states purpose and input sources, the second lists parameters. Every sentence adds value, and the purpose is front-loaded.

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 covers the tool's context (when to use), inputs, and outputs (percentiles, parameters, KS goodness). With an output schema available, no further detail is needed for 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?

The description includes an Args section that explains both parameters in plain language, adding meaning beyond the schema titles. For eur_values it says 'List of EUR values' and for distribution it specifies allowed values 'lognormal or normal', which compensates for the lack of schema descriptions.

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 fits a statistical distribution (lognormal or normal) to EUR values to compute P10/P50/P90 percentiles, distinguishing it from sibling tools like fit_decline which focus on decline curve analysis.

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 specifies the tool is for EUR values from Monte Carlo, bootstrapping, or analog wells, providing clear context. It does not explicitly state when not to use it, but the purpose is well-defined.

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