What can you do with this server?

blackmount-nlp-mcp is a lightweight, dependency-free NLP server providing 45+ text analysis tools, covering: * Tokenization: Split text into words/sentences; generate word-level or character-level n-grams. * Readability: Flesch Reading Ease, Flesch-Kincaid Grade, Gunning Fog, Coleman-Liau, ARI, SMOG, syllable counts, and a full readability report via get_reading_level. * Sentiment Analysis: Compound score (−1 to +1), positive/negative/neutral label, per-sentence breakdown, and aspect-based sentiment. * Keyword Extraction: TF-IDF (corpus-level), RAKE (key phrases), top words by frequency, and top n-gram phrases. * Text Similarity: Jaccard similarity, cosine similarity, Levenshtein edit distance (raw and normalized), and Longest Common Subsequence length. * Text Cleaning: Remove stopwords (500+ built-in), punctuation, numbers, URLs, emails, or HTML tags; normalize whitespace; lowercase; Porter stemming; and a configurable multi-step cleaning pipeline. * Detection & Statistics: Detect language (18 languages with confidence scores), identify character encoding/script type, get English confidence score, and count words, sentences, and paragraphs with average lengths. * Summarization: Extractive summarization (top-scored sentences) and a full document profile including word count, readability scores, language, and estimated reading time.

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blackmount-nlp-mcp

by BlackMount-ai

Overview Schema Related Servers Score Discussions

Python

Remote

blackmount-nlp-mcp

PyPI version License: MIT Python 3.10+

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+.

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.

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.

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"

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}]

Development

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

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.

License

MIT

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

3dRelease cycle

2Releases (12mo)

Resources

GitHub Repository

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