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8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

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

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

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

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "semantic-intelligence": {
      "type": "remote",
      "url": "https://viseon.io/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "semantic-intelligence": {
      "type": "http",
      "url": "https://viseon.io/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=semantic-intelligence&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vdmlzZW9uLmlvL21jcCJ9"
}

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

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

extensions:
  "semantic-intelligence":
    enabled: true
    name: "semantic-intelligence"
    type: streamable_http
    uri: "https://viseon.io/mcp"
    timeout: 300

Quality 72 / 100needs work

protocol100 · weight 25

pass
protocol.modernsupports 2026-07-28 (server/discover)
pass
protocol.statelessanswers without a session
pass
protocol.transportstreamable HTTP
pass
protocol.list_ttltool list is cacheable (ttlMs 3600000)

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene71 · weight 30

pass
tools.descriptionsevery tool has a description
pass
tools.description_lengthdescriptions are concise
pass
tools.schemasevery tool has an object input schema
fail
tools.annotationsno tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
fail
tools.directory_hintsno tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: search_content (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); query_relationships (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); fuzzy_find_and_traverse (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
pass
tools.token_costabout 954 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability25 · weight 15

warn
stability.changes8 tool changes in 30 days
fail
stability.rug_pull4 tools kept their name but changed most of their description in 30 days; review before trusting them

dependenciesn/a · weight 15

n/a
deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

7 tools, about 954 tokens to load them all · tool set 2a3308ac3bd4 · tools last changed 2026-10-13 · checked 2026-10-20 11:40 UTC

Tool changes

2026-10-13get_citable_passagesadded
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, an article's @id, page url or title, to look inside that article only. limit: 1 to 5 passages, default 3. The text is verbatim and unaltered: no model has read or paraphrased it. It matches the article's own words, so ask in the words the article would use; for what the graph says about an article (description, author, date, topics) use fuzzy_find_and_traverse and get_entity.
2026-10-11fuzzy_find_and_traversechangeddescription, input schemameaning changed
before
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.
after · 43% of words in common
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 match score) or full (every property and relationship). A client on protocol 2026-07-28 or later gets summary unless it asks for full; an older client gets full unless it asks for summary.
2026-10-10traverse_pathadded
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). Literal values (dates, names, emails) named in a hop are returned in values. Example, who founded the company Hamza works for and when: start "Hamza Ashraf", path [["worksFor"], ["founder", "foundingDate"]]. Without path, follows every link for maxHops (1-3, default 2) and returns links only, with a budget of 100 rows shared between hops, so hubs are cut short rather than hiding later hops (use a path to go deeper: a path call may return up to 400 rows). Returns edges [{hop, from, predicate, to, to_name, to_types}], values [{hop, subject, subject_name, predicate, value}], truncated and truncated_hops (the hops that hit a limit).
2026-10-05fuzzy_find_and_traversechangeddescription, input schema
before
Find and rank schema.org entities from natural language. Returns bounded entity context with relationship IDs; use get_entity for full detail.
after · 84% of words in common
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.
2026-10-05search_contentchangeddescriptionmeaning changed
before
Full-text search across graph
after · 20% of words in common
Substring search across the text of every entity; returns up to 20 entity URLs.
2026-10-05find_by_typechangeddescription
before
Find all entities of a specific schema.org type
after · 56% of words in common
Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).
2026-10-05query_relationshipschangeddescriptionmeaning changed
before
Find entities connected to a subject
after · 24% of words in common
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.
2026-10-05get_entitychangeddescription, input schemameaning changed
before
Get complete schema.org entity data by URL
after · 44% of words in common
Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction.

Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 96 / 100

provenance85

liveness100

freshness100

hygiene100

+repository +dns-namespace +website +reachable +uptime-99% +updated-15d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://viseon.io/mcp` streamable-http	none	yes	99%	618 ms	2026-07-28	2026-10-20

Tools (7)

`find_by_type`	Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).
`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 match score) or full (every property and relationship). A client on protocol 2026-07-28 or later gets summary unless it asks for full; an older client gets full unless it asks for summary.
`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, an article's @id, page url or title, to look inside that article only. limit: 1 to 5 passages, default 3. The text is verbatim and unaltered: no model has read or paraphrased it. It matches the article's own words, so ask in the words the article would use; for what the graph says about an article (description, author, date, topics) use fuzzy_find_and_traverse and get_entity.
`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.
`search_content`	Substring search across the text of every entity; returns up to 20 entity URLs.
`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). Literal values (dates, names, emails) named in a hop are returned in values. Example, who founded the company Hamza works for and when: start "Hamza Ashraf", path [["worksFor"], ["founder", "foundingDate"]]. Without path, follows every link for maxHops (1-3, default 2) and returns links only, with a budget of 100 rows shared between hops, so hubs are cut short rather than hiding later hops (use a path to go deeper: a path call may return up to 400 rows). Returns edges [{hop, from, predicate, to, to_name, to_types}], values [{hop, subject, subject_name, predicate, value}], truncated and truncated_hops (the hops that hit a limit).

Schemas: list_tools.

Similar MCP servers

Official server.json

{
  "name": "io.viseon/semantic-intelligence",
  "title": "VISEON Ask",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://viseon.io/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "6.0.15",
  "repository": {
    "url": "https://github.com/VISEONIO/VISEONIO",
    "source": "github"
  },
  "websiteUrl": "https://viseon.io/viseon-for-web/",
  "description": "VISEON's Schema.org knowledge graph: products, services, people, FAQs and terms, from viseon.io"
}