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LiLara-AI

ShadowGraph

Official

shadowgraph_rebuild

Read-onlyIdempotent

Rebuilds a projection from the store's journal to report whether the fold completed, leaving journal and live graph untouched.

Instructions

Replay this store's own journal into a projection and report whether the fold was complete. shadowgraph_journal reads the entries themselves, shadowgraph_validate diagnoses the live graph. Reads only: the journal is untouched and the live graph is not replaced by the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requireFullHistoryNoWhen true, refuse to rebuild if pre-journal metadata-only entries exist, returning rebuildable:false instead of a fold that silently starts later. Defaults to false.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesTrue when the fold ran; it does not by itself mean the projection is complete.
legacyYesEntries recognised as pre-journal or non-replayable, each with a why.
reasonYesWhy the projection is not rebuildable, or null when it is.
appliedYesEntries folded into the projection.
skippedYesEntries not folded, each carrying seq, type, and a stable why such as unknown_entry_type or unsupported_schema_version.
duplicatesYesSequence numbers appearing more than once, each { seq, count }. A repeated sequence cannot be totally ordered, so it makes the fold untrustworthy.
projectionYesThe projection folded from the journal.
replayedToYesHighest sequence folded.
rebuildableYesTrue only when every entry in the replay range was folded and the result is trustworthy.
journalEpochYesFirst replayable sequence.
replayedFromYesLowest sequence folded.
Install Server

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds concrete context: 'the journal is untouched and the live graph is not replaced by the result.' This is especially useful because 'rebuild' could otherwise imply destructive replacement. No contradiction with annotations exists.

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: the main action, sibling differentiation, and read-only clarification are all included without redundancy. The most important information is front-loaded.

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?

The tool has an output schema, a single optional parameter that is fully documented, and annotations covering safety and idempotency. The description adds the necessary scoping and no-side-effect guarantees, so an agent has everything needed to call this tool correctly.

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 sole parameter, requireFullHistory, is fully documented in the schema with a clear explanation of true/false behavior and its return effect. The description itself adds no parameter-specific information, but with 100% schema coverage, the baseline of 3 is appropriate.

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 opens with a specific verb and resource: 'Replay this store's own journal into a projection and report whether the fold was complete.' It further distinguishes itself from shadowgraph_journal and shadowgraph_validate, so an agent can tell exactly which operation this tool performs.

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 names two sibling tools and contrasts them with this one: shadowgraph_journal reads entries, shadowgraph_validate diagnoses the live graph, while shadowgraph_rebuild replays the journal into a projection. This gives clear guidance on when to choose this tool versus 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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