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Train Neo4j Knowledge Graph From Text (Async Task)

lyzr_kg_ext_train_text_task_neo4j

Ingest text into a Neo4j knowledge graph asynchronously. Specify source, RAG ID, and optional schema to train the graph with new knowledge.

Instructions

Ingest text into the Neo4j knowledge graph as an async task (namespaced endpoint).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText content to ingest
rag_idYesThe ID of the RAG system to train
sourceYesSource label for the ingested text
extra_infoNoExtra metadata to attach to the ingested text
extra_fieldsNoAny additional fields merged into the request body
schema_promptNoAllowed nodes and relationships for the knowledge graph
Behavior3/5

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

Annotations already declare readOnly=false, idempotent=false, and destructive=false. The description adds the async behavior, which is not covered by annotations, but it doesn't elaborate on implications like returning a task ID or needing to poll status. It adds some value but is thin.

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

Conciseness4/5

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

The description is a single, front-loaded sentence with no filler. It efficiently communicates the core action and a key differentiator ('async task'), though it is quite brief. It earns a 4 for being appropriately sized without wasting words.

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

Completeness2/5

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

There is no output schema, and the description does not explain the return value, how to monitor the async task, or what 'namespaced endpoint' means. It also doesn't mention related status-checking tools (e.g., lyzr_kg_task_status). Given the tool's async nature, this leaves significant gaps for the agent.

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% description coverage for all 6 parameters, so the schema already handles parameter semantics. The description adds no additional parameter context, so the baseline of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the action (Ingest text) and resource (Neo4j knowledge graph), and differentiates itself with 'async task' and 'namespaced endpoint'. However, it doesn't explicitly contrast with the synchronous variant (lyzr_kg_ext_train_text_neo4j) beyond the async aspect, so it's not a perfect 5.

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

Usage Guidelines3/5

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

The phrase 'as an async task' implies when to use this tool (when non-blocking execution is desired) but does not explicitly name alternatives or state when not to use it. It lacks clear exclusion criteria or comparison to sibling tools like lyzr_kg_train_text or lyzr_kg_ext_train_text_neo4j.

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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