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Train Neo4j Knowledge Graph From Website

lyzr_kg_ext_train_website_neo4j

Crawl website URLs and ingest content into a Neo4j knowledge graph to train a RAG system, enabling knowledge extraction from web sources.

Instructions

Crawl and ingest websites into the Neo4j knowledge graph (namespaced endpoint).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesURLs to crawl and ingest
actorNoApify actor to use (default "apify/website-content-crawler")
rag_idYesThe ID of the RAG system to train
sourceNoSource label for the ingested content (default "website")
chunk_sizeNoChunk size for splitting content (default 1000)
crawler_typeNoCrawler type (default "cheerio")
extra_fieldsNoAny additional fields merged into the request body
chunk_overlapNoChunk overlap for splitting content (default 100)
max_crawl_depthNoMaximum crawl depth (default 0)
max_crawl_pagesNoMaximum number of pages to crawl (default 1)
dynamic_content_wait_secsNoSeconds to wait for dynamic content (default 5)
Behavior2/5

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

Annotations already provide safety hints (readOnly=false, not destructive). The description only states the action and does not reveal additional behavior like whether it overwrites data, how long it takes, or whether it requires an existing RAG/agent. It adds little beyond the 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 one short, front-loaded sentence with no filler. It is concise but perhaps too sparse for a complex tool.

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?

The tool has 11 parameters, nested objects, and no output schema. The description does not mention return values, side effects, prerequisites, or what 'namespaced endpoint' implies. It is not complete enough for an agent to fully understand the tool's behavior.

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?

All 11 parameters have schema descriptions (100% coverage), so the description does not need to explain them. The schema already defines required fields and defaults. The description adds no parameter semantics, so baseline 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 uses a specific verb ('crawl and ingest') and names the target resource ('Neo4j knowledge graph'), plus a distinguishing detail ('namespaced endpoint') that separates it from generic KG train tools. However, the meaning of 'namespaced endpoint' is left undefined, which slightly reduces clarity.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool over its siblings such as lyzr_kg_train_website or lyzr_kg_ext_train_text_neo4j. The 'namespaced endpoint' hint is implicit but does not constitute clear usage instructions.

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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