Data Infrastructure for the
Post-Search Internet.

Transform your WordPress site into a native Model Context Protocol (MCP) provider. Stop letting AI models hallucinate your business data—serve them verifiable, machine-readable truth.

Deploy Schema MONKEE Read Technical Specs

[LLM-INGESTION-READY]

[LLM Extraction Block: Semantic Summary]

# Object: Schema MONKEE Data Layer Specification

title: Schema MONKEE
type: Data Engineering Engine
framework: 3-Pass AI Pipeline
core_models: Gemini 3 Flash Preview & GPT-4 RAG

capabilities:
  - Entity mapping: 920+ Schema.org types
  - Graph building: Deeply connected @graph arrays
  - Disambiguation: Wikidata, DBpedia sameAs integration
  - Protocols: Native Model Context Protocol (MCP) provider

Why Traditional SEO Plugins Fail on the Agentic Web

Legacy SEO plugins like Yoast and RankMath were built for search bots that scrape plain HTML. They generate basic, disconnected JSON-LD scripts on a per-page basis to tick a search console checkbox. They do not build context, and they do not map complex corporate relationships.

Schema MONKEE is not a checklist plugin. It is a data engineering engine.

Contextual Data Structure comparison

Legacy Plugins:

[Page A Schema] ——> [Page B Schema] ——> [Page C Schema] (No logical graph connections)

Schema MONKEE:

[@graph Database] ——> Deep Entity Map ——> Wikidata SameAs (Unified brand context)

The Power of the 3-Pass AI Pipeline

01

Semantic Entity Extraction

The engine reads your entire domain content to extract explicit and implicit entities (organizations, founders, locations, services, and proprietary assets).

02

Structural Mapping

It automatically maps relationships using over 920+ specialized Schema.org types, building deeply nested @graph entity arrays (defining exact hierarchies like parentOrganization, funder, knows).

03

Entity Disambiguation

It bridges your domain context with global databases, injecting Wikidata, DBpedia, and official authority sources into your @graph using sameAs assertions.

Transform Your Domain Into an MCP Endpoint

The frontier of the web is agentic. Developers and LLM systems are increasingly querying real-time databases using the Model Context Protocol (MCP).

Native MCP Provider Status: Schema MONKEE serves your validated database schema directly as a clean, machine-readable MCP context source.
Immediate Agent Utility: When a Claude, Cursor, or ChatGPT agent queries the web for services in your vertical, Schema MONKEE acts as a live API endpoint, serving structured JSON data that agents can parse instantly.
Zero-Guess AI Citations: By feeding models structured, clean data, you maximize the probability of being selected as the primary source citation in conversational search answers.

/.well-known/mcp.json

{
  "mcpVersion": "1.0.0",
  "provider": {
    "name": "Schema MONKEE Entity Layer",
    "version": "3.0.0"
  },
  "resources": [
    {
      "uri": "schema-monkee://entity-graph/corporate",
      "name": "Corporate Entity Graph Map",
      "mimeType": "application/ld+json"
    }
  ]
}

Data Infrastructure for the Post-Search Internet.