Blackmount Nlp Mcp

What is Blackmount Nlp Mcp?

Blackmount Nlp Mcp is a lightweight MCP server that provides 45 classical NLP tools for any MCP client. It is a pure Python implementation with no heavy dependencies (stdlib + regex only), delivering a 42KB wheel. It is designed for developers who need fast, deterministic text processing without large model downloads or API keys.

How to use Blackmount Nlp Mcp?

Install via pip install blackmount-nlp-mcp or run directly with uvx blackmount-nlp-mcp without installation. Configure your MCP client (e.g., Claude Desktop, Cursor) by adding the tool to its MCP server configuration with the command uvx and args ["blackmount-nlp-mcp"]. Invoke any of the 45 available tools through the MCP client interface.

Key features of Blackmount Nlp Mcp

• 45 classical NLP tools with zero heavy dependencies
• Pure Python implementation using stdlib and regex
• Porter stemmer, TF-IDF, RAKE, and Levenshtein distance
• VADER-style sentiment analysis with 2000+ word lexicon
• Readability scores: Flesch, Gunning Fog, Coleman-Liau, ARI, SMOG
• Language detection across 18 languages via n-gram profiles

Use cases of Blackmount Nlp Mcp

• Analyze sentiment and subjectivity in user feedback or reviews
• Extract keywords and key phrases from documents using TF-IDF or RAKE
• Assess readability levels of text for content optimization
• Detect the language of short text snippets
• Clean and normalize text by stripping HTML, expanding contractions, and removing stopwords

FAQ from Blackmount Nlp Mcp

What is the difference between Blackmount Nlp Mcp and libraries like spaCy or transformers?

Blackmount Nlp Mcp implements all NLP functionality from scratch using Python stdlib and regex, requiring no large model downloads or external data. The package is 42KB, with zero heavy dependencies, making installation fast and deterministic.

What runtime dependencies are required?

The server has zero heavy dependencies—it uses only Python's standard library and the regex module. No API keys or external services are needed.

Where does my data go when using this server?

All processing happens locally on your machine. The server is deterministic, requires no internet connection for model downloads, and does not send data to any external API.

What are the known limitations?

The implementation uses classical NLP techniques (e.g., Porter stemmer, n-gram language detection) rather than modern neural approaches. It is deterministic and fast but may not match the accuracy of large deep learning models on complex tasks.

What transport or authentication does the server use?

The server runs locally via the MCP protocol using stdio transport. It requires no authentication or API keys, as it is a self-contained local tool.