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trw_learn_update

Update an existing engineering learning record to reflect resolved issues, obsolete patterns, or improved insights, and adjust its recall ranking through feedback signals.

Instructions

Update an existing learning — status, fields, or feedback signal.

Use when:

  • The issue a learning describes has been fixed (status="resolved").

  • A pattern is no longer applicable (status="obsolete").

  • Detail or summary can be sharpened now that root cause is clearer.

  • You want to boost/demote an entry's recall ranking via feedback.

Output: dict with fields {status: "updated"|"not_found"|"invalid", error?: str, field_updated?: str}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoReplace the entry's tag set. Passing `[]` clears all tags. Callers are responsible for dedup/normalization.
typeNoUpdated type — "incident", "pattern", "convention", "hypothesis", or "workaround".
detailNoUpdated detail text (replaces existing detail).
domainNoUpdated domain tags.
impactNoUpdated impact score (0.0-1.0).
statusNoNew status — "active", "resolved", or "obsolete". Resolved/obsolete entries stop appearing in recall.
expiresNoUpdated expiration date/condition.
summaryNoUpdated summary text (replaces existing summary).
feedbackNoSignal whether this learning was helpful or unhelpful — "helpful" or "unhelpful". Affects recall ranking via feedback-aware decay (PRD-CORE-132).
task_typeNoUpdated task type identifier.
assertionsNoReplace assertions on this entry (PRD-CORE-086 FR12). Empty list removes all.
confidenceNoUpdated confidence — "unverified", "low", "medium", "high", or "verified".
nudge_lineNoUpdated nudge text (max 80 chars, auto-truncated).
supersedesNoid of a PRIOR learning that THIS learning replaces/corrects (PRD-CORE-194 FR04). Closes the prior record's validity window (sets its invalid_from + invalidated_by=this id) and RETAINS it — never a delete. Fires ONLY when explicitly passed; a routine field edit never closes a window.
learning_idNoID of the learning to update (e.g., "L-abc12345").
team_originNo
phase_originNo
phase_affinityNoUpdated phase affinities.
protection_tierNoUpdated protection tier.
Behavior4/5

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

No annotations are provided, so the description carries full burden. It details effects: updating status stops entries from appearing in recall, feedback affects ranking, and the supersedes parameter closes prior validity windows. It also describes the output shape. However, it lacks information on authorization or rate limits, which would be needed for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear opening sentence followed by bullet points for usage. It is well-structured and front-loaded with the core purpose. No extraneous information is present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 19 parameters and no output schema, the description covers key aspects: when to use, effects of parameters (especially supersedes and feedback), and the output structure with status and error fields. However, it could mention idempotency or whether updates are partial or replace all fields, but the schema descriptions handle most parameter details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 89%, so baseline is 3. The tool description adds value by explaining how parameters like feedback affect recall ranking and how supersedes closes validity windows. This context goes beyond the schema descriptions, justifying a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Update an existing learning — status, fields, or feedback signal' with specific verb and resource. It lists concrete use cases (e.g., fixing a learned issue, marking obsolete, sharpening details) which make the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists when to use the tool with bullet points: status transitions, detail refinement, feedback adjustment. This provides clear context for invocation, even though it doesn't explicitly mention when not to use it or alternatives.

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

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