nexo_learning_apply_retroactively

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 discloses that the tool 'creates deterministic NF-RETRO-L<learning>-D<decision> followups so the helper is idempotent across reruns' and explains the dry_run parameter behavior. It does not mention permissions, side effects, or safety, but the core behavioral traits are covered.

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 well-structured: a single purpose sentence, followed by a brief implementation detail (Fase 2 item 3), usage guidance, and behavioral note on idempotency. Every sentence adds value, and the total is under four lines. No fluff or repetition.

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 has 5 parameters and no output schema. The description covers the main intent, usage scenario, and key behavioral traits (idempotency, followup naming). It does not specify the return format (e.g., count of matches or followups created), which would be helpful but is not critical. Overall, it provides sufficient context for an agent to use the tool 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 100%, so the baseline is 3. The description does not add significant meaning beyond the schema for individual parameters. It provides overall context but lacks additional detail for each parameter. The schema already describes each parameter with clear names and defaults, so the description's contribution is minimal.

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's purpose: 'Scan recent decisions and surface those that conflict with a learning's prevention rule.' It uses a specific verb (scan, surface) and resource (decisions related to a learning's prevention rule). It differentiates from sibling tool nexo_learning_add by noting that the latter automatically invokes this tool when a prevention field is present.

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: 'Use this when you add a new rule and want to retroactively check whether past decisions still hold.' It also explains when not to use manually: 'nexo_learning_add invokes this automatically when the new learning has a prevention field — call this tool manually only when you want to re-scan with a longer window or a different threshold.' This provides clear guidance on alternatives.

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