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Dannet MCP server

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by kuhumcst·io.github.kuhumcst/dannet·v1.0.2·29 stars
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DanNet - Danish WordNet with rich lexical relationships and SPARQL access.

B80/100grade B
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B80/100

full report

Adoption
Growing

29 stars

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Dannet tools (6, 1 write)

write = sends, deletes, buys or posts
  • autocomplete_danish_wordFree

    Danish words starting with `prefix` (at least 3 characters), at most `max_results`, as {"autocompletions": [...]} where lemmas are plain strings and inflected forms of a matching lemma are [lemma, form] pairs.

  • fetch_ddo_definitionFree

    The full definitions of `synset_id` from DDO (ordnet.dk), where DanNet's own skos:definition may be truncated. Follows the dns:source of each sense. Returns the DanNet definition, the DDO definitions found and any errors; DDO and DanNet have drifted apart, so some source URLs no longer resolve.

  • get_entity_infoFree

    Full JSON-LD of a DanNet resource: a synset ("synset-3047"), word ("word-11021628") or sense ("sense-21033604"), given bare or prefixed ("dn:synset-3047"), or an external resource by prefix ("ili:i76470", "ontolex:LexicalConcept"). Properties use prefixed names (wn:hypernym, ontolex:isEvokedBy, ...); language-tagged values are {"@value": ..., "@language": ...}.

  • get_word_overviewFree

    Every sense of the Danish `word` in one call: a list with synset_id, label, definition, lexfile, ontological_types, synonyms (words sharing the synset) and hypernym ({synset_id, label} or null) per synset. Only synsets where the word itself has a sense count, not multi-word expressions containing it.

  • get_word_synsetsFree

    The synsets (senses) of the Danish `word`, as JSON-LD entries with @id (e.g. "dn:synset-3047" for get_entity_info), skos:definition, dns:ontologicalType and wn:lexfile. A word with a single synset gives that synset's full JSON-LD as the only entry.

  • sparql_querywrite actionFree

    Run a SPARQL SELECT `query` against DanNet and return standard SPARQL JSON results ({"head": ..., "results": {"bindings": [...]}}). The common prefixes (dn, dns, dnc, dnf, wn, ontolex, lexinfo, skos, rdfs, rdf, owl, marl, dc, ili, en, enl, cor) are declared automatically. `timeout` is in ms (max 15000), `max_results` at most 100, `distinct` adds DISTINCT, and `inference` selects the model: None tries the base model and retries with inference on an empty result; True forces inference, which inverse relations like wn:hyponym and wn:holo_* need; False forces the base model. Performance rules: anchor every query on a known URI or a word lookup (dn:synset-3047 wn:hypernym ?x, never ?x wn:hypernym ?y alone); never FILTER(CONTAINS(...)) over all labels, look the word up first; make every triple pattern share a variable with another; add LIMIT; prefer VALUES over FILTER for several known URIs; the store also holds the English WordNet (en:), so anchor on dn: or "..."@da to stay in Danish. Templates: Synsets of a word: SELECT DISTINCT ?synset ?label WHERE { ?entry ontolex:canonicalForm/ontolex:writtenRep "hund"@da . ?entry ontolex:sense/ontolex:isLexicalizedSenseOf ?synset . ?synset rdfs:label ?label } Taxonomic ancestors: SELECT DISTINCT ?ancestor ?label WHERE { dn:synset-3047 wn:hypernym+ ?ancestor . ?ancestor rdfs:label ?label } Hyponyms (needs inference=True, or query the inverse wn:hypernym): SELECT DISTINCT ?hyponym ?label WHERE { ?hyponym wn:hypernym dn:synset-3047 . ?hyponym rdfs:label ?label } Ontological types (an RDF bag): SELECT ?type WHERE { dn:synset-3047 dns:ontologicalType/?pos ?type . FILTER(STRSTARTS(STR(?pos), STR(rdf:_))) } Taxonomic similarity (dnf:path, dnf:lch, dnf:wup score two synsets of the same language and part of speech, 1.0 for identical): SELECT ?synset ?score WHERE { ?synset a ontolex:LexicalConcept . FILTER(STRSTARTS(STR(?synset), STR(dn:))) BIND(dnf:wup(dn:synset-3047, ?synset) AS ?score) } ORDER BY DESC(?score) LIMIT 20

Public scan report

scanner v0.1.10 · 2026-10-01 · same rubric, same numbers if you re-run it

1 high1 low
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 1636ms20/20
  • Tool poisoning6 tool descriptions checked13/15
  • Auth qualityopen endpoint exposes 1 write-action tools with no auth3/15
  • Maintenancelast push 0 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10

Findings (2)

  • highWrite-action tools reachable without authenticationauth.open-write
  • lowUnusually long tool description (over 2,000 characters)poison.long-description
    tool sparql_query: …Run a SPARQL SELECT `query` against DanNet and return standard SPARQL JSON results ({"head": ..., "results": {"bindings": [...]}}). The common prefixes (dn, dns, dnc, dnf, wn, ontolex, lexinfo, skos, rdfs, rdf, owl, marl, dc, ili, en, enl, cor) are declared automatically. `timeout` is in ms (max 15000), `max_results` at most 100, `distinct` adds DISTINCT, and `inference` selects the model: None tries the base model and retries with inference on an empty result; True forces inference, which inverse relations like wn:hyponym and wn:holo_* need; False forces the base model. Performance rules: anchor every query on a known URI or a word lookup (dn:synset-3047 wn:hypernym ?x, never ?x wn:hypernym ?y alone); never FILTER(CONTAINS(...)) over all labels, look the word up first; make every triple pattern share a variable with another; add LIMIT; prefer VALUES over FILTER for several known URIs; the store also holds the English WordNet (en:), so anchor on dn: or "..."@da to stay in Danish. Templates: Synsets of a word: SELECT DISTINCT ?synset ?label WHERE { ?entry ontolex:canonicalForm/ontolex:writtenRep "hund"@da . ?entry ontolex:sense/ontolex:isLexicalizedSenseOf ?synset . ?synset rdfs:label ?label } Taxonomic ancestors: SELECT DISTINCT ?ancestor ?label WHERE { dn:synset-3047 wn:hypernym+ ?ancestor . ?ancestor rdfs:label ?label } Hyponyms (needs inference=True, or query the inverse wn:hypernym): SELECT DISTINCT ?hyponym ?label WHERE { ?hyponym wn:hypernym dn:synset-3047 . ?hyponym rdfs:label ?label } Ontological types (an RDF bag): SELECT ?type WHERE { dn:synset-3047 dns:ontologicalType/?pos ?type . FILTER(STRSTARTS(STR(?pos), STR(rdf:_))) } Taxonomic similarity (dnf:path, dnf:lch, dnf:wup score two synsets of the same language and part of speech, 1.0 for identical): SELECT ?synset ?score WHERE { ?synset a ontolex:LexicalConcept . FILTER(STRSTARTS(STR(?synset), STR(dn:))) BIND(dnf:wup(dn:synset-3047, ?synset) AS ?score) } ORDER BY DESC(?score) LIMIT 20…
Overall 80/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Dannet compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
80/100
higher than 60% of 48,255 servers
Tools
6
the typical server has 7
Tools that change things
17%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote server answers 100%
Response time
958 ms
faster than 12% of 30,031 servers
GitHub stars
29
more than 92% of 28,688 servers

What its tools let an agent do: 5 read · 1 change

Install Dannet in Claude Code, Cursor or VS Code

claude mcp add --transport http dannet https://wordnet.dk/mcp
Add to Cursor

What the publisher says

From the Dannet repository's README, as published. We do not edit it. Read it on GitHub

DanNet is a WordNet for the Danish language. DanNet uses RDF as its native representation at both the database level, in the application space, and as its primary serialisation format.

  • Browse the data at wordnet.dk
  • Query the data by integrating with AI
  • Download datasets from the releases page

Table of Contents

  • Dataset Formats
  • Companion Datasets
  • Standards
  • LLM Integration (AI)
  • Implementation
  • Setup
  • Deployment
  • Database Release Workflow

Dataset Formats

DanNet is available in multiple formats to maximise compatibility:

Example: Using DanNet with Python

import wn

wn.add("dannet-wn-lmf.xml.gz")

for synset in wn.synsets('kage'):
    print((synset.lexfile() or "?") + ": " + (synset.definition() or "?"))

Differences Between Formats

While every format includes all synsets/senses/words, the CSV, WN-LMF and DMLex variants do not include every data point:

  • CSV: Some data is lost when converting from an open graph to fixed tables.
  • WN-LMF: Only official GWA relations are included per the standard (proprietary DanNet relations from the DanNet schema are excluded).
  • DMLex: Combines DanNet with COR, DDS and COR.SEM in one file. Relations to other datasets are not included. See doc/dmlex/plan.md for the conversion rules.

For the complete dataset, use the RDF format or browse at wordnet.dk.

Companion Datasets

Several companion datasets expand the RDF graph with additional data:

Inferred Data

Additional data is implicitly inferred from the base dataset, companion datasets, and ontological metadata. These inferences can be browsed at wordnet.dk. Releases containing fully inferred graphs are specifically marked as such.

Standards

DanNet is based on the Ontolex-lemon standard combined with relations defined by the Global Wordnet Association as used in the official GWA RDF standard.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Dannet: common questions

Is Dannet MCP server safe?
Mostly: it is graded B (80/100). Read the Dannet safety report
How do I install Dannet?
It runs remotely at wordnet.dk. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does Dannet need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is Dannet maintained?
The last commit was 34 days ago (2026-10-01). The latest release is v1.0.2.
Is Dannet up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
How does Dannet compare with other MCP servers?
safety score 80/100 (higher than 60% of 48,255 servers); tools 6 (the typical server has 7); tools that change things 17% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.