noul

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the output format (JSON string with specific fields), the threshold semantics (>= 0.5 leans yes), and performance characteristics ('fast, cheap'), which is useful. However, it omits details like error behavior, rate limits, or authentication requirements. While these are not critical for a simple query tool, the description is not exhaustive, warranting a score of 3.

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 compact and well-structured: it leads with the core purpose, then usage examples, a clear exclusion, and a concise Args/Returns breakdown. Every sentence adds value, and there is no redundancy or filler. The structure is front-loaded and easy to scan for a quick decision.

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 the tool's simplicity (2 params, output schema present), the description covers all essential aspects: what it does, when to use it, what parameters mean, and what to expect in the return value. The output is described structurally, and the tool's scope is well-defined. There is no missing information that would prevent an agent from invoking it 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 0%, so the description is the sole source of parameter meaning. It clearly defines 'question' as a yes/no question with an example, and 'context' as optional background facts with a length guideline ('Keep it short'). This compensates for the lack of schema documentation, though it could go further (e.g., explaining how context affects output). Still, it provides meaningful semantics beyond the bare schema field names.

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 states a specific action ('Ask Jev a yes/no question'), defines the input (yes/no question) and output (0-1 degree with threshold), and gives concrete examples. It clearly communicates the tool's core function and distinguishes it from general LLM use by explicitly stating it is for binary checks only. Even without naming siblings, the purpose is unambiguous.

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 explicitly states when to use this tool ('fast, cheap binary checks') with real examples, and when not to use it ('NOT for questions that need multi-step reasoning') with a clear alternative ('use an LLM'). This provides strong usage guidance that helps an agent select this tool over alternatives.

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