nexo_learning_apply_retroactively

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

No annotations provided, so description carries full burden. It discloses deterministic followup creation (idempotent behavior), automatic invocation by sibling, and parameters controlling behavior. No mention of destructive actions, but likely not destructive. Covers key traits adequately.

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?

Five sentences, well-structured with purpose first, then usage, then technical details. No unnecessary words; every sentence adds value.

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?

No output schema, and the description does not explain return values or what exactly is surfaced. It mentions creating followups but not what the tool returns to the caller. Lacks completeness for a scanning tool with 5 parameters.

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 parameters are already well-documented. The description adds context by explaining the purpose of lookback_days, max_matches, min_score, and dry_run, but does not significantly enhance meaning beyond the 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 states the tool scans recent decisions and surfaces conflicts with a learning's prevention rule. It distinguishes from sibling nexo_learning_add by clarifying that manual use is for re-scanning with different parameters, ensuring no ambiguity.

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?

Explicit usage guidance: use when adding a new rule to retroactively check past decisions. Also clearly states that nexo_learning_add invokes this automatically, so manual calling is only for re-scanning with a longer window or different threshold.

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