eur_distribution

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

With no annotations, the description carries the full burden. It describes inputs and outputs but does not disclose whether the tool has side effects, requires specific permissions, or is purely computational. For a stateless tool, this is adequate but not exemplary.

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 paragraph captures the purpose, while the second adds parameter details. It avoids verbosity, though the 'Args:' section is somewhat redundant given the schema.

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?

Given two parameters and no annotations, the description covers inputs, possible distributions, and expected outputs (percentiles, parameters, KS test). It is complete for a simple fitting tool, though it could mention error handling or edge cases.

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 0%, but the description explains both parameters: eur_values is a list of numbers, distribution is 'lognormal' or 'normal'. It adds meaning beyond the schema which only provides titles and types. The default value is not explicitly stated but implied.

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 to EUR values for P10/P50/P90, and specifies lognormal or normal distribution. It differentiates itself from sibling fitting tools by focusing on EUR distribution for percentiles.

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?

No explicit guidance on when to use this tool versus alternatives (e.g., fit_decline). The description implies it is for EUR distribution fitting but does not provide context for choosing lognormal vs normal or when to prefer this over other fitting methods.

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