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LiLara-AI

ShadowGraph

Official

shadowgraph_traverse

Read-onlyIdempotent

Walk relationship links from an entity ID to reveal connected memory nodes within a project and scope.

Instructions

Walk relationships outward from one entity id and return what is reached. Find ids first with shadowgraph_search or shadowgraph_recall; shadowgraph_retrieve searches content and adds neighbours. Reads only. Memory outside the requested project and scope stays hidden.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesEntity id to start from. Must exist.
depthNoHow many relationship hops to follow, 1-10. Defaults to 1. Anything outside the range is rejected.
scopeNoScope selector. Omitted or partial fields mean explicit nulls, not "any": identity is the exact (project, userId, agentId, runId, memoryType, key) tuple, so a run-scoped memory never leaks into a user-only read.
projectNoProject whose memory nodes are visible during the walk. Defaults to "default"; decisions, facts, and attempts are not filtered by it.
relationNoFollow only relationships with this exact name. Omit to follow all of them.
directionNoout follows relationships whose from is in the frontier, in follows their to, both follows either. Defaults to both.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootYesThe entity the walk started from.
depthYesThe hop limit that was applied.
nodesYesEntities reached, root first. Mixed kinds, including alternatives synthesised from their decision.
directionYesThe direction that was applied.
relationsYesRelationships traversed. Named relations, not "edges".
Install Server

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds a meaningful behavioral boundary: memory outside the requested project and scope stays hidden. This goes beyond the annotations and clarifies the privacy/visibility model.

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 tightly written sentences with no filler. The purpose is front-loaded, followed by sibling routing, then the read-only and scope-boundary behavior. Every sentence earns its place.

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?

With a rich output schema and strong annotations, the description covers what is needed for selection and invocation: prerequisites, alternative tools, and the scope-privacy boundary. Minor gaps like result shape are already addressed by the output schema.

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?

Schema description coverage is 100%, and each parameter already has detailed semantic documentation. The description only adds context for the starting id; depth, scope, relation, and direction semantics are fully handled by the schema, so baseline 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?

Description states a specific action and resource: walking relationships outward from one entity id and returning what is reached. It also distinguishes itself from siblings by directing the agent to shadowgraph_search or shadowgraph_recall for finding ids and noting shadowgraph_retrieve's content-plus-neighbors behavior.

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

Usage Guidelines4/5

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

Provides clear routing guidance: find ids first with shadowgraph_search or shadowgraph_recall, and use shadowgraph_retrieve when content search plus neighbors is needed. It does not exhaustively state when not to use the tool, but the context is strong enough for correct selection.

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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