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classic-nlp-mcp

classic-nlp-mcp

by liftkkkk
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Python
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    TDQS

    A3.7/5.0

    Scored across 15 tools

    Disambiguation4/5

    Each tool targets a clearly distinct NLP capability, and the descriptions actively distinguish the near-neighbors (e.g. dependency_parse vs constituency_parse, noun_phrases vs keyword_extract vs openie_triples). The only mild overlap is between the individual analyzers and annotate_full, which is explicitly framed as an aggregator.

    Naming Consistency4/5

    All names are snake_case, which is consistent, and most combine a domain noun with an operation (pos_tag, sent_split, seme_lookup, wordnet_similarity). A few deviate from a strict verb_noun pattern (plain 'tokenize', 'ner', 'sentiment' as bare capability names), but the set is still highly predictable.

    Tool Count5/5

    Fifteen tools sit squarely in the well-scoped range for an NLP toolkit, and each maps to a distinct capability (plus one legitimate aggregate). Nothing feels redundant or padded.

    Completeness4/5

    The surface covers a full classic NLP pipeline: tokenization, POS/lemma, NER, keywords, sentence splitting, constituency/dependency parsing, noun phrases, OpenIE, sentiment, and lexical semantics, plus a batch aggregator. Minor gaps remain (no standalone language detection, coreference, or summarization/classification beyond sentiment), but core workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing

    Resources