random_interval

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

There are no annotations provided, so the description carries the full burden. It discloses important edge behaviors: integer includes both endpoints, float may include upper bound due to rounding, equal bounds return that value, and integer bounds must be whole numbers. This is good context, but it lacks details on error handling for invalid bounds (e.g., min > max) or the exact distribution (uniform is stated). It is above average but not comprehensive.

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 and structured: three short sentences. It starts with the core functionality, then provides important edge case details. Every sentence adds value. It could be slightly more structured with bullet points for edge cases, but it is efficient and front-loaded with the main purpose.

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 tool is relatively simple with 3 parameters elementary types. The description covers the key behavioral aspects: sampling uniform, handling of endpoints, equal bounds, and integer bounds constraints. With an output schema present, it doesn't need to explain return values. Potential missing pieces include error handling for invalid ranges (e.g., min > max) and note that the tool does not support other distributions, but given the simplicity, the description is reasonably complete.

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%, meaning the schema provides no description for the parameters (minimum, maximum, kind). The description compensates by explaining the meaning of kind (integer includes endpoints, float may include upper bound), and that equal bounds return that value, and integer bounds must be whole numbers. However, it does not explain the semantics of minimum and maximum beyond being bounds, but that is self-evident from their names. Given the 0% coverage, the description does a good job of adding necessary semantics.

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 that it samples uniformly between bounds, with a kind parameter for integer or float. It distinguishes itself from siblings by explaining the interval sampling behavior, though it could explicitly mention that it's a uniform distribution, which is implied. Given the sibling tools (random_number, random_normal, roll_dice), the description is clear enough about its purpose and domain.

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 explains the behavior of sampling between bounds and the kind parameter, but it does not explicitly state when to use this tool over its siblings, such as random_number, random_normal, or roll_dice. It implies use for uniform random values in an interval, but does not provide explicit exclusions or alternatives. This is a moderate gap.

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