# VISEON Ask

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

- name: io.viseon/semantic-intelligence
- version: 6.0.15
- connection class: R0 (autonomous)
- trust: 96/100
- quality: 72/100 (needs work)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.viseon%2Fsemantic-intelligence
- tools: https://api.protogrid.dev/v1/servers/io.viseon%2Fsemantic-intelligence/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 100
- liveness: 100
- freshness: 100
- provenance: 85
- drivers: +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.

## Quality 72/100 (needs work)

### protocol: 100 (weight 25)

- pass `protocol.modern`: supports 2026-07-28 (server/discover)
- pass `protocol.stateless`: answers without a session
- pass `protocol.transport`: streamable HTTP
- pass `protocol.list_ttl`: tool list is cacheable (ttlMs 3600000)

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 71 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- fail `tools.annotations`: no tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
- fail `tools.directory_hints`: no tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: search_content (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); query_relationships (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_entity (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
- pass `tools.token_cost`: about 954 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 25 (weight 15)

- warn `stability.changes`: 8 tool changes in 30 days
- fail `stability.rug_pull`: 4 tools kept their name but changed most of their description in 30 days; review before trusting them

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 7, about 954 tokens to load them all
- tool set hash: 2a3308ac3bd4a61f4f0b59247abea83a
- tools last changed: 2026-10-13T13:07:41.341Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.viseon/semantic-intelligence.svg)](https://protogrid.dev/servers/io.viseon/semantic-intelligence)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-13 `get_citable_passages` added
  - 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.
- 2026-10-11 `fuzzy_find_and_traverse` changed (description, inputSchema) **meaning 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: 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.
- 2026-10-10 `traverse_path` added
  - 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).
- 2026-10-05 `fuzzy_find_and_traverse` changed (description, inputSchema)
  - before: Find and rank schema.org entities from natural language. Returns bounded entity context with relationship IDs; use get_entity for full detail.
  - after: 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.
- 2026-10-05 `search_content` changed (description) **meaning changed**
  - before: Full-text search across graph
  - after: Substring search across the text of every entity; returns up to 20 entity URLs.
- 2026-10-05 `find_by_type` changed (description)
  - before: Find all entities of a specific schema.org type
  - after: Find all entities of a specific schema.org type (exact type name, for example Service, Person, DefinedTerm).
- 2026-10-05 `query_relationships` changed (description) **meaning changed**
  - before: Find entities connected to a subject
  - after: 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.
- 2026-10-05 `get_entity` changed (description, inputSchema) **meaning changed**
  - before: Get complete schema.org entity data by URL
  - after: Get complete schema.org entity data by URL (its @id), including relationships the graph holds in either direction.

Full history: https://api.protogrid.dev/v1/servers/io.viseon%2Fsemantic-intelligence/changes

## Remotes

- https://viseon.io/mcp (streamable-http, auth none, reachable true, uptime30d 0.98888886)

## Packages

_none_

## 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).