"semantic-release" matching MCP servers:

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.

distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.

Provides intelligent manuscript analysis and writing assistance for markdown projects, including semantic search, quality checks, terminology consistency, link validation, progress tracking, and comprehensive writing statistics.

MCP server providing semantic text analysis and compression tools via Hypernym AI's API.

Enables AI agents to query and recall your entire Gmail inbox locally, providing fast summarization and semantic search without relying on Gmail's search API.

Converts raw GitHub notes into polished Polish release notes, PR descriptions, and changelogs using predefined workflows.

Enables LLM clients to analyze numerical data series (NumPy, Pandas, Polars, lists) and receive token-efficient natural-language summaries of trend, volatility, anomalies, seasonality, and data quality instead of raw values. Supports single-series analysis, batch processing of multiple series, and structured JSON output.

Enables LLMs to search, analyze, and summarize academic research papers in real-time from arXiv, Semantic Scholar, and PubMed. Provides automatic deduplication, citation analysis, and BibTeX generation across multiple research databases.

Enables researchers to interact with a personal corpus of academic papers through semantic search, grounded question answering with source citations, and document summarization or comparison via MCP tools.

Exposes locally-built AI tools to Claude, enabling article summarization, promo generation, and semantic search over an Obsidian vault.

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

Parses WeChat public account articles to extract metadata, content, and generate AI-powered summaries with semantic analysis, viewpoint extraction, and structure analysis capabilities.

Enables semantic search, retrieval, and summarization of Gmail emails with privacy-aware classification and PII sanitization.

Enables semantic search over personal files using natural language, with optional AI summarization, all running locally.

Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.

A Model Context Protocol server that provides document analysis capabilities to LLM applications, including extraction, chunking, summarization, and semantic search for PDF, DOCX, and plaintext documents.

Semantic compression MCP server that reduces document token usage by 60-80% using structured symbolic notation, enabling Claude to efficiently store, retrieve, diff, and summarise documents across PRD, CODE, PAPER, and MEETING domains.