run_single

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

Since no annotations are provided, the description carries the full burden. It discloses the verdict logic: pass only if the model clearly refuses, otherwise needs_review. It also mentions returning an error string on failure. This is sufficient for a simple run tool without side effects.

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 reasonably concise, with a docstring-like structure (Args, Returns). The main action is front-loaded. Some minor redundancy exists (e.g., backticks), but overall it is efficient.

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, the description covers the essential points: what it does, the parameters, the return type (report dict or error), and the verdict behavior. No output schema exists, but the description adequately describes the output for typical use.

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%, so the description must compensate. It does so by explaining that 'model' is the name of an installed Ollama model and references 'list_models'. For 'prompt', it says 'the single prompt to send'. This adds meaning beyond the schema's type-only specification.

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 runs one ad-hoc prompt against a model and scores the response. The verb 'run' and resources 'prompt' and 'model' are specific. It distinguishes from sibling tools like 'run_probe' by emphasizing 'ad-hoc' (no fail markers).

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?

It explains when to use this tool: for ad-hoc prompts without fail markers. It implies that if fail markers are present, one would use a different tool like 'run_probe'. However, it does not explicitly state when NOT to use it or name alternatives directly.

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