run_probe

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

No annotations are provided, so the description carries the full burden. It discloses the return format (report dict or error string) but does not mention side effects, idempotency, safety, or resource usage. This is adequate but not thorough.

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 uses a structured format with Args and Returns sections, making it easy to parse. Each sentence adds value, though it could be slightly more concise without losing clarity.

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 output schema, the description explains the return shape (model, scope, summary, per-probe results) and error condition. It references sibling tools for validation, covering most contextual needs, but lacks explicit prerequisites.

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 compensates well. It explains 'model' as 'Name of an installed Ollama model' and 'category' as 'A probe category name', each with references to listing tools. This adds meaning beyond the bare schema.

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 specifies the verb 'Run every probe', the resource 'category against model', and the outcome 'score the results'. It distinguishes itself from sibling tool 'run_single' by implying it runs all probes in a category.

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 tells when to use the tool by listing required arguments and referencing sibling tools 'list_models' and 'list_probes' for valid values. It does not explicitly state when not to use it (e.g., for a single probe) but the context implies alternatives.

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