In Memoria is an MCP server that enables AI coding assistants such as Claude or Copilot to retain, recall, and provide context about codebases across sessions. It learns patterns, architecture, and conventions from user code, offering persistent intelligence that eliminates repetitive explanations and generic suggestions. Through the Model Context Protocol, it allows AI tools to perform semantic search, smart file routing, and track project-specific decisions efficiently.

• ⭐ 94
• MCP
• pi22by7/In-Memoria

Mem0 MCP Server implements a Model Context Protocol-compliant server for storing, retrieving, and searching coding preferences. It integrates with Mem0 and offers tools for persistent management of code snippets, best practices, and technical documentation. The server exposes an SSE endpoint for clients like Cursor, enabling seamless access and interaction with coding context data.

• ⭐ 506
• MCP
• mem0ai/mem0-mcp

MCP Zotero provides a Model Context Protocol server enabling AI models such as Claude to access and interact with Zotero libraries. Users can securely link their Zotero accounts and perform actions including listing collections, retrieving papers, searching the library, and getting details about specific items. Integration is designed for both standalone operation and as an extension for tools like Claude Desktop.

• ⭐ 137
• MCP
• kaliaboi/mcp-zotero

Markmap MCP Server enables seamless conversion of Markdown content into interactive mind maps using the Model Context Protocol (MCP). It leverages the open-source markmap project and provides users with diverse export formats including PNG, JPG, and SVG. Designed for easy integration with MCP clients, it offers tools for automated browser previews, rich interactivity, and batch mind map generation. The server can be installed easily via npm or Smithery and supports configurable output directories.

• ⭐ 137
• MCP
• jinzcdev/markmap-mcp-server

RAG Documentation MCP Server provides vector-based search and retrieval tools for documentation, enabling large language models to reference relevant context in their responses. It supports managing multiple documentation sources, semantic search, and real-time context delivery. Documentation can be indexed, searched, and managed with queueing and processing features, making it highly suitable for AI-driven assistants. Integration with Claude Desktop and support for Qdrant vector databases is also available.

• ⭐ 238
• MCP
• hannesrudolph/mcp-ragdocs

Graphlit MCP Server provides a Model Context Protocol interface, enabling seamless integration between MCP clients and the Graphlit platform. It supports ingestion from a wide array of sources such as Slack, Discord, Google Drive, email, Jira, and GitHub, turning them into a searchable, RAG-ready knowledge base. Built-in tools allow for document, media extraction, web crawling, and web search, as well as advanced retrieval and publishing functionalities. The server facilitates easy configuration, sophisticated data operations, and automated notifications for diverse workflows.

• ⭐ 369
• MCP
• graphlit/graphlit-mcp-server

GistPad MCP is an MCP server designed for managing, organizing, and sharing personal knowledge, daily notes, reusable prompts, and more through GitHub Gists. It integrates with clients like the GistPad VS Code extension and web apps, allowing users to interact with their gists using standardized MCP tools across MCP-enabled AI products. The project features comprehensive gist management, daily notes, collaboration, and prompt creation to streamline knowledge workflows with AI-enabled context. Seamless onboarding is supported via personal access tokens and simple configuration.

• ⭐ 168
• MCP
• lostintangent/gistpad-mcp

Zettelkasten MCP Server provides an implementation of the Zettelkasten note-taking methodology, enriched with bidirectional linking, semantic relationships, and categorization of notes. It enables creation, exploration, and synthesis of atomic knowledge using MCP for AI-assisted workflows. The system integrates with clients such as Claude and supports markdown, advanced search, and a structured prompt framework for large language models. The dual storage architecture and synchronous operation model ensure flexibility and reliability for managing personal or collaborative knowledge bases.

• ⭐ 114
• MCP
• entanglr/zettelkasten-mcp

MemoryMesh is a knowledge graph server designed to help AI models maintain structured, consistent memory, especially for interactive storytelling and RPG contexts. It is based on the Model Context Protocol (MCP), as explicitly stated, and retains core MCP server functionalities. By utilizing dynamic, schema-based configuration, the server enables creation and management of nodes and relationships, offering comprehensive tools for data integrity, feedback, and event tracking. MemoryMesh emphasizes flexibility, supporting both predefined and dynamic schemas for guiding AI interactions.

• ⭐ 313
• MCP
• CheMiguel23/MemoryMesh

OpenZIM MCP Server provides structured, intelligent access to ZIM-format knowledge bases, enabling large language models to efficiently search, navigate, and understand content in offline archives. Dual operation modes allow support for both advanced and simple LLM integrations. It features smart navigation by namespace, context-aware discovery, intelligent search, and relationship mapping to optimize knowledge extraction and utilization.

• ⭐ 8
• MCP
• cameronrye/openzim-mcp

mcp-server-chatsum is an MCP Server designed to summarize and query chat messages. It provides tools to interact with chat data, enabling users to extract and summarize message content based on specified prompts. The server can be integrated with Claude Desktop and supports communication over stdio, offering dedicated debugging tools via the MCP Inspector. Environment variable support and database integration ensure flexible deployment for chat data management.

• ⭐ 1,024
• MCP
• chatmcp/mcp-server-chatsum

ApeRAG is a production-ready Retrieval-Augmented Generation (RAG) platform that integrates graph-based, vector, and full-text search capabilities. It enables the construction of knowledge graphs and supports MCP (Model Context Protocol), allowing AI assistants direct interaction with knowledge bases. Features include advanced document parsing, multimodal processing, intelligent agent workflows, and enterprise management tools. Deployment is streamlined via Docker and Kubernetes, with extensive support for customization and scalability.

• ⭐ 920
• MCP
• apecloud/ApeRAG

MCP Content Summarizer Server provides intelligent summarization of various content types including text, web pages, PDF documents, and EPUB books using Google's Gemini 1.5 Pro model. Through the Model Context Protocol, it supports customizable, multi-language summaries with options for style and focus. It is designed for integration with applications as an MCP server and offers tools for both summarization and testing. The solution maintains key information while producing concise and context-aware summaries from diverse content sources.

• ⭐ 142
• MCP
• 0xshellming/mcp-summarizer

MCP Tic-Tac-Toe is a server implementation that provides a complete set of MCP tools for playing, managing, and analyzing tic-tac-toe games. It supports interactions with AI assistants such as Claude, enabling features like creating multiple parallel sessions, making moves, providing strategic analysis, and managing game context. The server is designed for easy integration with clients through various transport methods, including stdio and SSE, and supports seamless AI-human collaboration.

• ⭐ 2
• MCP
• tomholford/mcp-tic-tac-toe

mcp-stockfish provides a server implementing the Model Context Protocol (MCP) to enable seamless integration between AI models and the Stockfish chess engine. It supports multiple concurrent sessions, full UCI command support, and offers both stdio and HTTP server modes. Built for robust, concurrent usage, it handles session and command management, exposes a JSON-based API response, and offers Docker support for easy deployment.

• ⭐ 4
• MCP
• sonirico/mcp-stockfish