blackmount-nlp-mcp 0.1.3

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

blackmount-nlp-mcp

NLP for MCP — zero heavy dependencies. Built by Blackmount.

45 text analysis tools as a FastMCP server. No NLTK. No spaCy. No transformers. One dependency (mcp[cli]), under 50 KB of NLP code, ready in seconds. Requires Python 3.10+.

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Why this exists

	blackmount-nlp-mcp	NLTK	spaCy	transformers
Wheel size	42 KB	1.5 MB	6 MB+ (+ models)	10 MB+ (+ models)
Direct dependencies	1	many	many	many
Tokenization	✅	✅	✅	✅
Sentiment analysis	✅	✅	❌	✅
Readability scores	✅	❌	❌	❌
Keyword extraction	✅	✅	❌	❌
Text similarity	✅	✅	✅	✅
Language detection	✅ (18 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

Claude Desktop

Add to your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

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

Cursor

Add to .cursor/mcp.json in your project root:

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

Any MCP client

The server runs over stdio. Point your client at the blackmount-nlp-mcp command:

blackmount-nlp-mcp

Restart your editor. All 45 NLP tools are now available — just ask in natural language.

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Tool catalog

Tokenization (4 tools)

Tool	Description	Try asking
word_tokenize	Split text into words, handling contractions and punctuation	"Tokenize this paragraph into words"
sentence_tokenize	Split into sentences, handling common abbreviations	"Break this text into individual sentences"
generate_ngrams	Generate word-level n-grams from a token list	"Generate bigrams from these tokens"
generate_char_ngrams	Generate character-level n-grams	"Get character trigrams for this word"

Readability (8 tools)

All readability functions return 0.0 for empty or whitespace-only input.

Tool	Description	Try asking
flesch_reading_ease	0–100 ease score (higher = easier)	"Calculate the Flesch Reading Ease score"
flesch_kincaid_grade	US grade level estimate	"What grade level is this written at?"
gunning_fog_index	Fog index based on complex word ratio	"Calculate the Fog index for this text"
coleman_liau_index	Coleman-Liau grade-level index	"Get the Coleman-Liau score"
automated_readability_index	ARI grade-level index	"What's the ARI for this document?"
smog_grade_index	SMOG grade (recommended for healthcare text)	"Calculate the SMOG grade for this document"
count_syllables	Syllable count estimation for any word	"How many syllables in 'extraordinary'?"
get_reading_level	All readability scores in one call with a plain-English label	"Give me a full readability report for this text"

Sentiment Analysis (4 tools)

Tool	Description	Try asking
get_sentiment_score	Compound sentiment score from −1.0 to +1.0	"What's the sentiment of this customer review?"
get_sentiment_label	Returns positive, negative, or neutral	"Is this feedback positive or negative?"
get_sentence_sentiments	Per-sentence sentiment breakdown	"Show me the sentiment of each sentence"
get_aspect_sentiment	Sentiment scoped to specific topics	"What's the sentiment around 'pricing' in these reviews?"

Keyword Extraction (4 tools)

Tool	Description	Try asking
extract_tfidf_keywords	TF-IDF keyword ranking across a corpus	"What are the key terms across these docs?"
extract_rake_keywords	RAKE algorithm — phrase-level keyword extraction	"Extract the key phrases from this article"
get_word_frequency	Top words by frequency, stopwords excluded	"What are the most common words in this text?"
get_phrase_frequency	Top n-gram phrases by frequency	"What two-word phrases appear most often?"

Text Similarity (5 tools)

Tool	Description	Try asking
get_jaccard_similarity	Word-set overlap, 0–1	"How similar are these two paragraphs?"
get_cosine_similarity	Bag-of-words cosine similarity, 0–1	"Calculate cosine similarity between these texts"
get_edit_distance	Levenshtein edit distance	"How many edits to turn 'kitten' into 'sitting'?"
get_normalized_edit_distance	Edit distance normalized to 0–1	"How different are these two strings?"
get_longest_common_subsequence	LCS length between two strings	"What's the LCS length of these two strings?"

Text Cleaning (10 tools)

Tool	Description	Try asking
clean_remove_stopwords	Strip 500+ English stopwords	"Remove stopwords from this text"
clean_remove_punctuation	Remove all punctuation	"Strip the punctuation"
clean_remove_numbers	Remove numeric tokens	"Remove all numbers from this"
clean_remove_urls	Strip URLs	"Clean out the URLs"
clean_remove_emails	Strip email addresses	"Remove email addresses from this text"
clean_remove_html	Strip HTML tags	"Strip the HTML from this content"
clean_normalize_whitespace	Collapse and trim whitespace	"Normalize the whitespace"
clean_lowercase	Lowercase the text	"Convert this to lowercase"
porter_stem	Porter stemmer (pure Python, no NLTK)	"Stem the word 'running'"
clean_text_pipeline	Configurable multi-step cleaning in one call	"Clean this text: remove HTML, URLs, and stopwords"

Detection (8 tools)

Tool	Description	Try asking
detect_text_language	Identify language from 18 supported languages	"What language is this text written in?"
detect_text_encoding_type	Detect script: ASCII, Latin, Cyrillic, CJK, Arabic	"What script does this text use?"
check_is_english	English confidence score, 0–1	"Is this text in English?"
count_words	Word count	"How many words are in this?"
count_sentences	Sentence count	"Count the sentences"
count_paragraphs	Paragraph count	"How many paragraphs?"
get_avg_word_length	Mean word length in characters	"What's the average word length?"
get_avg_sentence_length	Mean sentence length in words	"How long are the sentences on average?"

Summarization (2 tools)

Tool	Description	Try asking
get_extractive_summary	Select the N highest-scoring sentences from a document	"Summarize this article in 3 sentences"
get_text_statistics	Full document stats: words, readability, language, reading time	"Give me a statistical profile of this text"

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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))
# 0.9285

print(sentiment_label(text))
# 'positive'

print(reading_level(text))
# {'grade_level': 12.39, 'label': 'college',
#  'flesch_reading_ease': 14.27, 'flesch_kincaid_grade': 12.39,
#  'gunning_fog': 19.58, 'coleman_liau': 10.94,
#  'automated_readability': 7.51, 'smog_grade': 11.21}

print(rake_keywords(text))
# [{'phrase': 'absolutely amazing', 'score': 4.0},
#  {'phrase': 'product', 'score': 1.0},
#  {'phrase': 'quality', 'score': 1.0},
#  {'phrase': 'excellent', '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 built by Blackmount — tools for people who work with AI.

blackmount-mcp — Browser memory, AI chat search, and session analytics as an MCP server. Pair it with blackmount-nlp-mcp to analyze your saved conversations: extract keywords from chat history, score readability of AI responses, detect sentiment trends across sessions.

app.blackmount.ai — The full Blackmount platform. Search, organize, and analyze everything your AI tools produce.

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License

MIT