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jsonld_entities

Extracts nested JSON-LD, validates Schema.org types and properties, and resolves disconnected blocks into one unified knowledge graph with typed edges.

Instructions

JSON-LD Entity Graph Resolver — Schema.org to Knowledge Graph

Extract deeply nested JSON-LD, validate against Schema.org (979 types, 1,633 properties), and resolve disconnected blocks into one unified knowledge graph with typed edges. No browser, no API keys, no LLM. First 3 pages free.

Pricing (pay per event, billed to your Apify account): Actor start: $0.00005; Page processed: $0.0015; platform usage billed to your account.

Runs on Apify as subimpact/jsonld-entity-graph-resolver.

Input Schema

NameRequiredDescriptionDefault
maxPagesNoHard limit on how many URLs this run may request. Leave empty for no limit.
_maxItemsNoHow many dataset items to return to the model (1-1000, default 50). Truncates the response only; it does not change what the Actor collects or charges for.
startUrlsYesList of pages to extract JSON-LD from. Each entry: {"url": "https://..."}
estimateOnlyNoRun the full extraction and report what it would cost, without charging anything.
maxChargeUsdNoHard ceiling on what this run may charge. Once reached, no further events are billed.
resolveGraphNoResolve @id references across all blocks on the page and emit a unified graph: nodes (id, types, properties) + edges (subject, predicate, object). Disconnected schemas become one graph.
_maxChargeUsdNoPer-run spending cap in USD (default 0.5). Passed to Apify as the run option maxTotalChargeUsd, so the run stops when it reaches this charge.
validateSchemaNoCheck every @type and property against the bundled Schema.org ontology (979 types, 1,633 properties). Reports unknown types and invalid properties per page.
includeRawBlocksNoAlso store the parsed JSON-LD blocks verbatim in each row (useful for debugging or downstream processing).
proxyConfigurationNoApify proxy for IP rotation. Leave default for automatic proxy.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose useful traits: no browser/API keys/LLM, Apify execution, free first pages, and per-event pricing. It still omits return shape, rate/error behavior, and account/auth prerequisites beyond the implication that billing is tied to an Apify account.

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 front-loaded with the core capability, followed by differentiators, pricing, and Actor identity. It is fairly tight for a complex tool, though the pricing and platform lines add some length that is only marginally useful to selection.

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

Completeness3/5

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

It conveys the main function, validation scope, graph-resolution behavior, and cost model. But for a ten-parameter tool with no output schema and no annotations, it should explain the dataset-item/result structure and validation-report shape more explicitly.

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%, so the schema already documents all ten parameters in detail. The main description does not map or add syntax for parameters like resolveGraph, validateSchema, estimateOnly, or maxChargeUsd, so the baseline of 3 applies.

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?

It uses specific verbs and resources: extract JSON-LD, validate against Schema.org, and resolve blocks into a unified knowledge graph. The JSON-LD entity-graph focus distinguishes it from generic page scrapers and SEO audit siblings.

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?

Usage is implied by the extraction/validation/graph-resolution purpose and the note that no browser, API keys, or LLM are needed. However, there is no explicit when-to-use, when-not-to-use, or comparison to alternatives such as web_page_markdown or seo_page_audit.

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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