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VISEON Ask

io.viseon/semantic-intelligence · v6.0.15

Measured by MCP Station

Description written by the publisher, not verified

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

Connect

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

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

Tools as reported by the server

• get_entity
  Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction.
• 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 description and the matc
• 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 for an organisation).
• 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). article: optional, a

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