Github IvanRublev Keyphrases McpGithub IvanRublev Keyphrases Mcp

This Keyphrases MCP Server is a natural language interface designed for agentic applications to extract keyphrasess from provided text. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to extract keyphrases more accurately and with higher relevance using the BERT machine learning model. It works directly with your local files in the allowed directories saving the context tokens for the LLM. The application ensures secure document processing by exposing only extracted keyphrases to the MCP client, not the original file content.

Using this MCP Server, you can ask the following questions:

Keyphrases help users quickly grasp the main topics and themes of a document without reading it in full and enable the following applications:

1. tags or metadata for documents, improving organization and discoverability in digital libraries 2. emerging trends, sentiment, identified from customer reviews, social media, or news articles 3. features or inputs for other tasks, such as text classification, clustering

The server uses a KeyBERT framework for the multi-step extraction pipeline combining spaCy NLP preprocessing with BERT embeddings:

1. Candidate Generation: KeyphraseCountVectorizer identifies meaningful keyphrase candidates using spaCy's en_core_web_trf model and discarding stop words 2. Semantic Encoding: Candidates and document are embedded using paraphrase-multilingual-MiniLM-L12-v2 sentence transformer 3. Relevance Ranking: KeyBERT calculates cosine similarity between candidate keyphrase and document embeddings 4. Diversity Selection: Maximal Marginal Relevance (MMR) ensures diverse, non-redundant keyphrases 5. Final Output: Top N most relevant and diverse keyphrases are selected and sorted alphabetically

See configuration document for details.