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LiLara-AI

ShadowGraph

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shadowgraph_review

List decisions whose rejected alternatives are due again from reopenWhen rules over stored facts, returning a JSON array of due decisions.

Instructions

List decisions whose rejected alternatives are due again, from reopenWhen rules over stored facts. shadowgraph_review_signals reads persisted ones, shadowgraph_ack_review closes one, shadowgraph_maintain ages first. Persists one signal per newly due decision, deduped by decision and reason; a repeat commits a revision. Returns a bare JSON array.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
factsNoFact key/value overrides evaluated instead of the stored facts of the same key. Stored facts are used for every key not listed here, so reopen rules still work after a restart.
projectNoReview only this project. Omit to review every project.
changedFactsNoFact keys that just changed. Only string-form reopenWhen rules match this list; it is an ephemeral signal, not durable state.
Install Server

TDQS

A4.5/5.0
Behavior5/5

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

Although the description begins with 'List,' it transparently discloses the write behavior: 'Persists one signal per newly due decision, deduped by decision and reason; a repeat commits a revision.' It also adds a return-format note, which is useful since no output schema exists. These details go well beyond the sparse annotations.

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 dense but well-organized: core purpose first, then sibling differentiation, then side-effect and return behavior. Every sentence adds distinct value without fluff or repetition of the schema.

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?

Despite the absence of an output schema, the description covers the essential behavioral context: what the tool lists, when signals persist, how deduplication works, what a repeat does, what it returns, and how it relates to sibling tools. An agent has enough to decide whether to invoke it and what to expect.

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 already describes all three parameters with 100% coverage, so the baseline is 3. The description does not add parameter-specific semantics, but the schema's descriptions are sufficient, including the ephemeral nature of changedFacts and the override behavior of facts.

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: 'List decisions whose rejected alternatives are due again, from reopenWhen rules over stored facts.' It clearly identifies the tool's core function and distinguishes it from shadowgraph_review_signals by noting that sibling reads persisted signals while this one lists newly due decisions.

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?

The description names relevant siblings and their roles ('shadowgraph_review_signals reads persisted ones, shadowgraph_ack_review closes one, shadowgraph_maintain ages first'), giving an agent a clear map of alternatives. However, it stops short of an explicit 'use this when / use that instead' conditional, leaving some inference to the agent.

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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