Keyphrases Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Keyphrases Mcp (Agent Skill) and scored it 45/100 (orange). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 1 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
A base64 string of 128+ characters appears in a documentation file. Encoded prompt injection hides the hostile instruction in base64 — invisible to keyword filters — and relies on the agent's ability to decode it at runtime. There is no normal authoring reason to embed a multi-hundred-byte base64 blob in skill docs.
*.sig, SIGNATURES) outside the documentation.Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Empowering LLMs with authentic keyphrase Extraction
Built with the following tools and technologies:
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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:
Autoregressive LLM models such as in Claude or ChatGPT process text sequentially, which—not only limits their ability to fully contextualize keyphrases across the entire document—but also suffers from context degradation as the input length increases, causing earlier keyphrases to receive diluted attention.
Bidirectional models like BERT, by considering both left and right context and maintaining more consistent attention across the sequence, generally extract existing keyphrases from texts more accurately and with higher relevance especially when no domain-specific fine-tuning is applied.
However, as autoregressive models adopt longer context windows and techniques such as input chunking, their performance in keyphrase extraction is improving, narrowing the gap with BERT. And domain-specific fine-tuning can make autoregressive LLM model to outperform the BERT solution.
This MCP server combines BERT for keyphrase extraction with an autoregressive LLM for text generation or refinement, enabling seamless text processing.
The server uses a KeyBERT framework for the multi-step extraction pipeline combining spaCy NLP preprocessing with BERT embeddings:
See configuration document for details.
This project is licensed under the MIT License.
For questions or support, reach out via GitHub Issues.
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