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LiLara-AI

ShadowGraph

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shadowgraph_record_outcome

Record how a decision turned out, adjust its confidence by evidence weight, and store lessons. Re-recording replaces the prior outcome, restamps it, and commits a revision.

Instructions

Record how a decision turned out and move its confidence by an evidence-weighted amount. shadowgraph_confidence_evidence records evidence short of an outcome, shadowgraph_update_status changes the lifecycle state. One outcome contribution per decision, so re-recording replaces rather than stacking, restamps it, and commits a revision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outcomeYesThe outcome to record.
decisionIdYesIdentifier of an existing decision, as returned by shadowgraph_record_decision, shadowgraph_search, or shadowgraph_retrieve.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesStable entity identifier.
goalNoWhat the decision was trying to achieve.
kindYesEntity kind: decision, attempt, memory, fact, relation, review, or alternative.
actorNoWho performed the write.
titleNoShort name of the decision.
chosenNoThe option that was chosen.
clientNoWhich client performed the write.
statusNoLifecycle state. Legacy records may carry a value this build does not recognise; shadowgraph_validate reports those.
outcomeNoThe recorded outcome, or null until one is recorded.
projectNoProject namespace; records imported from a schema that predates projects may carry null.
evidenceNoNormalised evidence entries: source, type, sourceClass, confidence, observedAt, detail.
createdAtNoISO 8601 creation time.
migrationNoPresent only on migrated records; records the legacy value a field was mapped from.
sessionIdNoSession identifier recorded with the write.
sourceRawNoThe original origin label when it differed from sourceClass. Audit only; never evidence.
updatedAtNoISO 8601 time of the last change.
confidenceNoAuditable confidence: initial, current (0-1), policy, a history entry per move, and a basis summarising the contributions it was folded from. Legacy records may lack basis.
supersedesNoIdentifiers of decisions this one replaced.
assumptionsNoAssumptions the decision rests on. Searchable content.
reviewAfterNoISO 8601 instant after which shadowgraph_maintain marks this decision stale.
sourceClassNoClaimed origin class recorded with the write. A claim, never proof.
alternativesNoRejected alternatives, each with id, label, reasonRejected, status, and the reopenWhen rules that make it reconsiderable.
supersededByNoIdentifier of the decision that replaced this one.
schemaVersionNoStorage schema version this entity was written under. A value above the build’s own version is preserved rather than downgraded.
failedAttemptsNoAttempt identifiers or notes attached to this decision.
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses important non-obvious behavior: one outcome contribution per decision, re-recording replaces rather than stacks, restamps the observation time, and commits a revision. This goes well beyond the bare annotations and gives the agent a realistic model of 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.

Conciseness5/5

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

Three sentences, each earning its place: purpose, sibling differentiation, and replacement semantics. The most important and distinguishing information is front-loaded, with no redundant filler.

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

Completeness5/5

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

For a two-parameter tool with a fully documented schema and an output schema, the description covers the essential behavioral context: what the tool does, how it differs from related tools, and the non-idempotent replacement behavior. Nothing critical is missing for safe invocation.

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

Parameters3/5

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

The input schema has 100% coverage with detailed descriptions for both decisionId and outcome, including how each status affects confidence and how sourceClass weights movement. The description adds only the general notion of evidence-weighted movement, which is a reasonable baseline since the schema carries the parameter detail.

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 states a specific action ('Record how a decision turned out') and the resulting effect ('move its confidence by an evidence-weighted amount'). It also explicitly differentiates itself from shadowgraph_confidence_evidence and shadowgraph_update_status, which helps an agent distinguish this tool from close siblings.

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 gives clear boundaries: shadowgraph_confidence_evidence is for evidence short of an outcome, shadowgraph_update_status is for lifecycle state changes, and this tool is for recording an outcome. It also notes that re-recording replaces prior recording, providing practical guidance for repeated use.

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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