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VISEON for Web

作者：VISEONIO版本 6.0.16i★ –i在云端运行
注册名称io.viseon/semantic-intelligence

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在云端运行
  • 传输方式: Streamable HTTP
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

你需要

你用的是哪个应用？

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址，然后点击 Add
  4. 如果要求登录，请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://viseon.io/mcp

在终端中运行这条命令

claude mcp add --transport http semantic-intelligence https://viseon.io/mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "semantic-intelligence": {
      "url": "https://viseon.io/mcp"
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "semantic-intelligence": {
      "type": "http",
      "url": "https://viseon.io/mcp"
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
  3. 粘贴下面的地址，起个名字，然后保存
  4. 如果要求登录，请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://viseon.io/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月9日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

  • get_entity

    Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction. Use this before selecting an entity's image or other media: retain the canonical subject ID and use only assets explicitly linked by the retrieved record. A matching

  • query_relationships

    Find entities connected to a subject: one row per property value, with object_type 'uri' for links to other entities and 'literal' for values. Optionally filter by predicate.

  • find_by_type

    Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).

  • search_content

    Substring search across the text of every entity; returns up to 20 entity URLs.

  • fuzzy_find_and_traverse

    Find and rank schema.org entities from natural language, tolerating typos and alternative names. Returns bounded entity context with relationship IDs; use get_entity for full detail. The optional detail argument is summary (for each hit: id, type, name, url, the start of the desc

  • traverse_path

    Walk the knowledge graph from one entity in a single call. start: an entity @id or a name (resolved like fuzzy_find_and_traverse). path: one entry per hop, each a predicate or a list of predicates; prefix a predicate with ^ to follow it backwards (e.g. ^worksFor finds who works f

  • get_citable_passages

    Return passages of the VISEON articles word for word, each with a link to the passage itself, the article it is part of and how much of the query it holds, so an answer can quote and cite them. query: the distinctive words asked about (a name, a term, a phrase the article uses).

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过域名 viseon.io 验证（注册中心会核实域名所有权）

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • ✓ 通过

    端点有响应

    在 317 毫秒内响应（HTTP 200）

  • – 不适用

    使用 OAuth 登录

    无需登录

  • ✓ 通过

    工具描述中没有隐藏指令

    已读取 7 个工具描述，未发现针对 AI 的隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 5752 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 5752 个服务器的 95% 更新
  • 基础检查结果等于或优于此分类中其他 5752 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

VISEON for Web 安全吗？

它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（6.0.16）于昨天进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

Up-to-date docs for AI agents

作者：upstash广泛使用↓ 97.3万/周★ 6.4万3天前更新

电脑或云端
安装

Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

作者：DeusData广泛使用↓ 1.8万/周★ 4.8万23天前更新

在你的电脑上运行
安装

Exa

Connect AI agents to Exa for web search, content fetching, and multi-step research.

作者：exa-labs广泛使用★ 52132个月前更新

在云端运行✓ exa.ai
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

VISEON

Semantic Intelligence · Discover | Discuss | Transact

*AI agents can't execute on canonical truth until you define it.*

VISEON is a Semantic Control Plane (operating system for AI) for governing organisational meaning and evidence across enterprise systems, websites and AI interfaces. It serves as the Governed Navigator for enterprise AI, giving autonomous engines and LLMs the explicit pathways, guardrails and auditability required to discover, discuss and transact with precision.

This repository is the public reference for VISEON. The platform itself runs at VISEON.IO, which is the canonical source for what VISEON is and does. Our ARD listing for the VISEON MCP capabilities and example questions can be found here VISEON-MCP

Use the VISEON MCP server

[`io.viseon/semantic-intelligence` — MCP developer guide](MCP-SERVER.md) covers the remote endpoint, transport, all seven tools, resources and prompt availability, authentication, VS Code / Copilot configuration and example prompts.

Connect to `https://viseon.io/mcp` using Streamable HTTP. Public knowledge-graph queries require no API key, account or paid plan. The ARD manifest provides complementary machine-readable discovery metadata.

One governed semantic ecosystem. Four complementary solutions.

Every VISEON solution is governed, built around authoritative business meaning, and MCP-addressable. All four solutions are live.

SolutionWhat it does
VISEON for WebMakes your brand, organisation, products and services understandable to AI through a governed digital twin and knowledge graph, accessible to visitors through VISEON Ask and to AI agents through MCP.
VISEON for EnterpriseEstablishes a governed Semantic Control Plane connecting enterprise systems, business definitions and relationships, giving AI agents authoritative context through MCP without replacing existing access controls.
VISEON for Qlik (vCat)Governs the meaning of your Qlik analytics estate, making applications, metrics, master items and business context discoverable and actionable by AI agents through MCP.
VISEON Data AssuranceEstablishes trust in enterprise data through governed quality, completeness and compliance checks, publishing verifiable evidence and data assurance status to AI agents through MCP.

Discover, Discuss, Transact

  • Discover. VISEON audits what public and internal AI architectures can currently see, identifies critical context gaps, and registers verified, brand-aligned facts into a unified context layer.
  • Discuss. Evidenced facts registered into a canonical context Ask/MCP layer let AI agents give accurate, authoritative answers across every touchpoint: web discovery, internal knowledge retrieval, or agent-to-agent execution.
  • Transact. The registry is connected through MCP and transient feeds, such as live prices and stock through change data capture, so agents can discover, recommend and act on it.

Semantic Entity Optimisation (SEO 3.0)

Semantic Entity Optimisation optimises the entity, not the page. Each thing an organisation is and offers gets one canonical definition, a stable identifier, verified links to the rest of the web and a record of where each fact came from, so AI reads the organisation from the source. It includes everything schema markup has always had to do, Google Rich Results included, and does not stop there.

Smarter.SEO is VISEON.IO for Qlik customers, existing and prospective, delivered by Differentia Consulting.

VISEON runs on VISEON

viseon.io is built the way VISEON builds for clients. Every page, product, service, person, glossary term and FAQ is an entity in one governed graph, served to people and to agents:

  • VISEON Ask answers visitors from the graph, with sources.
  • An MCP endpoint at viseon.io/mcp.
  • An Agentic Resource Discovery (ARD) manifest at /.well-known/ard.json.
  • A listing in the official MCP Registry as io.viseon/semantic-intelligence.
  • The knowledge graph catalog at /wp-json/viseon/v1/catalog and a schema.txt entity index, both advertised in the HTTP Link: headers and elements of every page.

Built on open standards

Schema.org, JSON-LD, the Model Context Protocol, NLWeb and Agentic Resource Discovery (ARD), connected to open commerce protocols including ACP, AP2 and Google's Universal Commerce Protocol. The graph is portable, inspectable and yours.

In this repository

  • README.md: this file.
  • Schema.org-JSON-LD-Edge-Integrity-Test.md: a reference prompt for checking that a Schema.org @graph is fully connected by bidirectional edges.

Other public repositories

RepositoryPurpose
qonnect-fixturesSynthetic test data for VISEON Qonnect and the VISEON DuckDB extension. No real customer data.
templatesIndustry Schema.org JSON-LD templates, with example organisations.
public_dataReference data files used by VISEON tooling.
agentfinder-catalogFork of GitHub's agent finder catalog, used to submit VISEON's MCP server and Differentia's services catalog upstream.

Built by Differentia Consulting

VISEON is developed by Differentia Consulting, enterprise data specialists since 2002 and a Qlik partner, who have been helping organisations prepare quality data models for business intelligence and AI. VISEON sits within the Smarter.BI solution family.

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