VISEON Ask

Name: VISEON Ask
Author: viseon

@viseonMCP server · External

VISEON's Schema.org knowledge graph: products, services, people, FAQs and terms, from viseon.io

Databasesv6.0.15

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One address for any MCP client. No sign-in needed on this server.

What it does

5 read1 other

Grouped by tool name. At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Tools

6 · grouped by tool name · checked 11 Oct 2026

Read · 5Look things up

get_entity
query_relationships
find_by_type
search_content
fuzzy_find_and_traverse

Other · 1Neither hinted nor named

traverse_path

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README4 min readView on GitHub

VISEON

Semantic Intelligence

Build Context. Command Visibility.

VISEON.IO is a governed data platform for AI discoverability, semantic search and agentic commerce. It turns a website into a complete knowledge graph, an MCP-ready data layer and an agentic commerce catalogue, so AI can discover, discuss and transact with brands, products and services.

This repository is the public face of VISEON.IO. It hosts reference material such as the Schema.org JSON-LD Edge Integrity Test. The platform itself runs at viseon.io.

Discover, Discuss, Transact

VISEON is delivered as a three-stage model that mirrors how AI now interacts with the web.

Discover

Make a brand instantly discoverable to AI agents. Audit Schema.org coverage across all domains, identify entity gaps, validate IDs and relationships, check Google Rich Result eligibility, and produce a comprehensive agentic catalogue that represents the business accurately.

A-Audit: Cross-domain schema artefacts → comprehensive, brand-representative agentic catalogue.

Discuss

Replace traditional site search with AI-powered conversations grounded in a governed knowledge graph. Agents answer customer questions accurately, agent-to-agent or human-facing, in the brand voice.

B-Build: Governed Schema knowledge graph → foundation for intent-based semantic search, whether agent-to-agent or human.

Transact

Connect the catalogue to commerce. Publish through structured data, APIs, MCP and NLWeb endpoints, and supported commerce protocols including ACP, AP2 and Google's Universal Commerce Protocol, so agents can browse, recommend and complete purchases.

C-Connect: Publish the catalogue and feed Ask-powered brand conversations and agentic transactions.

Why This Matters

Many brands remain digitally obscure to AI systems because they lack structured context and authoritative signals. When customers ask an AI assistant for a recommendation, better-defined competitors are surfaced instead. Traditional SEO alone is no longer enough.

VISEON addresses this by exposing the cohesive knowledge graph of an entire digital presence: a digital twin, language model optimised. The platform audits Schema.org markup, validates JSON-LD framework compliance, verifies Google Rich Results, and enables hybrid Vector and GraphRAG semantic search via the Model Context Protocol.

Standards and Protocols

VISEON is built on, and aligns to, open standards:

Schema.org as the canonical vocabulary
JSON-LD as the preferred serialisation, consistent with Google guidance and Microsoft NLWeb
Model Context Protocol (MCP) for governed, deterministic agent access
NLWeb for site-level conversational interfaces
ACP, AP2 and Google UCP for agentic commerce flows
YAML-LD and DCAT 3 application profiles for dataset description
Discovery Signals on VISEON.IO

VISEON.IO implements the same agent discovery signals it audits for clients. Every page exposes:

A <link rel="alternate" type="application/ld+json"> element pointing to the VISEON Knowledge Graph Catalog at /wp-json/viseon/v1/catalog, surfaced both in the HTML head and as an HTTP Link: header per RFC 8288.
A site-level schema.txt discovery file at the domain root, conforming to the schematxt v4 specification, also surfaced as an HTTP Link: header.

Together these let crawlers, AI agents and MCP-enabled clients locate the canonical structured data without scraping rendered HTML.

Capabilities

Cross-domain Schema.org audit and validation against the latest specifications
Entity disambiguation through sameAs, knowsAbout and Organization patterns
Google Rich Result eligibility checks across retained schema types
Knowledge graph completeness, accuracy and reachability scoring
FUSEON APIs for programmatic access to schema and entity data
MCP-ready data layer for direct agent consumption
Hybrid Vector and GraphRAG retrieval for semantic search

Who VISEON Is For

Growing enterprises that need a rapid, hands-off deployment of an AI discovery strategy
B2B organisations whose buyers research and shortlist vendors through AI assistants
International brands that require entity consistency across multiple regional domains
Institutions and content creators that need to protect intellectual property through semantic personification

Repository Contents

README.md: this file
Schema.org-JSON-LD-Edge-Integrity-Test.md: public reference for evaluating JSON-LD edge-case integrity in Schema.org implementations

Built by Differentia Consulting

VISEON is developed by Differentia Consulting, an 18-year Qlik Elite Partner that has been helping organisations prepare quality data models for business intelligence and AI use cases since 2002.

VISEON forms the AI discoverability layer of the Smarter.BI solution family. The Qlik-powered audit and governance interface is delivered as Smarter.SEO.