MCP Servers
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Memory & Knowledge
This project provides a toolset to crawl websites, wikis, tool/library documentations and generate Markdown documentation, and make that documentation searchable via a Model Context Protocol (MCP) server, designed for integration with tools like Cursor
mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving relevant information efficiently
Open-source MCP server that gives Claude persistent memory. Your AI keeps your standards, decisions, and reasons across sessions. Recalled by meaning, not keywords. Runs fully local: on-device semantic search, no cloud, no API keys. macOS, Windows, Linux. MIT.
The tool connects to your Substack/Medium blogs via their RSS feeds, fetches your posts, and permanently caches them locally. It also generates embeddings for each post, enabling semantic search to find the most relevant essays based on your queries.
It is a open-source universal memory engine where users can store and retrieve their AI conversations and context across different models. Users can add memories through MCP, SDK, or a website selfmemory.com Over time, this will evolve into a one-stop memory hub with note-taking
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
