MCP server

VISEON Ask Official

io.viseon/semantic-intelligence

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

viseon.io (verified by the MCP Registry)

Status

Working at last check

Tools

7

Token cost

About 1,175 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.semantic-intelligence]
url = "https://viseon.io/mcp"

Gemini CLI

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

Cursor

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

VS Code

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

Antigravity

agy mcp add semantic-intelligence https://viseon.io/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"semantic-intelligence","server_url":"https://viseon.io/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"semantic-intelligence","server_url":"https://viseon.io/mcp"}

Tools

• find_by_type ~69 tokens
  Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).
• fuzzy_find_and_traverse ~251 tokens
  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 description and the matc
• get_citable_passages ~261 tokens
  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). article: optional, a
• get_entity ~70 tokens
  Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction.
• query_relationships ~100 tokens
  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.
• search_content ~62 tokens
  Substring search across the text of every entity; returns up to 20 entity URLs.
• traverse_path ~362 tokens
  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 for an organisation).

Browse

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