blackmount-nlp-mcp 0.1.0

Text analysis and NLP MCP server — no NLTK, no spaCy, just results

blackmount-nlp-mcp

NLP for MCP — zero heavy dependencies.

45 text analysis tools delivered as a FastMCP server. No NLTK. No spaCy. No transformers. One dependency (mcp[cli]), under 50 KB of NLP code, ready in seconds.

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Why this exists

	blackmount-nlp-mcp	NLTK	spaCy	transformers
Package size	42 KB	60 MB+	200 MB+	2 GB+
Dependencies	1	many	many	many
Tokenization	✅	✅	✅	✅
Sentiment analysis	✅	✅	❌	✅
Readability scores	✅	❌	❌	❌
Keyword extraction	✅	✅	❌	❌
Text similarity	✅	✅	✅	✅
Language detection	✅ (20 langs)	❌	❌	❌

Everything is implemented from scratch in pure Python — Porter stemmer, TF-IDF, RAKE, Levenshtein, VADER-style sentiment, Flesch / Gunning Fog / Coleman-Liau / ARI / SMOG readability, extractive summarization, language detection — plus a built-in 2000+ word sentiment lexicon and 500+ stopword list, all baked into the package.

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Quick start

pip install blackmount-nlp-mcp

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "nlp": {
      "command": "blackmount-nlp-mcp"
    }
  }
}

Restart Claude Desktop. All 45 NLP tools are now available.

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Tool catalog

Tokenization (4 tools)

Tool	Description
word_tokenize	Split text into words, handling contractions and punctuation
sentence_tokenize	Split into sentences, handling common abbreviations
generate_ngrams	Generate word-level n-grams from a token list
generate_char_ngrams	Generate character-level n-grams

Readability (8 tools)

Tool	Description
flesch_reading_ease	0–100 ease score (higher = easier)
flesch_kincaid_grade	US grade level estimate
gunning_fog_index	Fog index based on complex word ratio
coleman_liau_index	Coleman-Liau grade-level index
automated_readability_index	ARI grade-level index
smog_grade_index	SMOG grade (recommended for healthcare text)
count_syllables	Syllable count estimation for any word
get_reading_level	All readability scores in one call with a plain-English label

Sentiment Analysis (4 tools)

Tool	Description
get_sentiment_score	Compound sentiment score from −1.0 (negative) to +1.0 (positive)
get_sentiment_label	Returns positive, negative, or neutral
get_sentence_sentiments	Per-sentence sentiment breakdown
get_aspect_sentiment	Sentiment score scoped to specific topics or keywords

Keyword Extraction (4 tools)

Tool	Description
extract_tfidf_keywords	TF-IDF keyword ranking across a corpus
extract_rake_keywords	RAKE algorithm — phrase-level keyword extraction
get_word_frequency	Top words by frequency, stopwords excluded
get_phrase_frequency	Top n-gram phrases by frequency

Text Similarity (5 tools)

Tool	Description
get_jaccard_similarity	Word-set overlap, 0–1
get_cosine_similarity	Bag-of-words cosine similarity, 0–1
get_edit_distance	Levenshtein edit distance
get_normalized_edit_distance	Edit distance normalized to 0–1
get_longest_common_subsequence	LCS length between two strings

Text Cleaning (10 tools)

Tool	Description
clean_remove_stopwords	Strip 500+ English stopwords
clean_remove_punctuation	Remove all punctuation
clean_remove_numbers	Remove numeric tokens
clean_remove_urls	Strip URLs
clean_remove_emails	Strip email addresses
clean_remove_html	Strip HTML tags
clean_normalize_whitespace	Collapse and trim whitespace
clean_lowercase	Lowercase the text
porter_stem	Porter stemmer (pure Python, no NLTK)
clean_text_pipeline	Configurable multi-step cleaning in one call

Detection (8 tools)

Tool	Description
detect_text_language	Identify language from 20 supported languages
detect_text_encoding_type	Detect script type: ASCII, Latin, Cyrillic, CJK, Arabic, etc.
check_is_english	English confidence score, 0–1
count_words	Word count
count_sentences	Sentence count
count_paragraphs	Paragraph count
get_avg_word_length	Mean word length in characters
get_avg_sentence_length	Mean sentence length in words

Summarization (2 tools)

Tool	Description
get_extractive_summary	Select the N highest-scoring sentences from a document
get_text_statistics	Full document stats: word count, readability, language, reading time

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Use as a library

The submodules are importable directly — no MCP server required:

from blackmount_nlp_mcp.sentiment import sentiment_score, sentiment_label
from blackmount_nlp_mcp.readability import reading_level
from blackmount_nlp_mcp.keywords import rake_keywords

text = "This product is absolutely amazing! The quality is excellent."

print(sentiment_score(text))   # 0.8745
print(sentiment_label(text))   # "positive"
print(reading_level(text))     # {"grade_level": 5.2, "label": "elementary", ...}
print(rake_keywords(text))     # [{"phrase": "product", "score": 1.0}, ...]

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Development

git clone https://github.com/BlackMount-ai/blackmount-nlp-mcp
cd blackmount-nlp-mcp
pip install -e .
pytest tests/ -v

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Blackmount ecosystem

blackmount-nlp-mcp is part of the Blackmount ecosystem.

Also check out blackmount-mcp — browser memory, AI chat search, and session analytics as an MCP server. Different audience, same zero-bloat philosophy.

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License

MIT