Zen MCP is a multi-model AI collaborative development server that provides enhanced workflow tools and cross-model context management for AI coding assistants such as Claude and Gemini CLI. It supports seamless collaboration of multiple AI models to complete development tasks such as code review, debugging, and refactoring, and can maintain the continuation of conversation context between different workflows.

MemoryMesh is a knowledge graph server designed for AI models, focusing on text role - playing games and interactive narratives. It helps AI maintain consistent and structured memory in conversations through dynamic schema definition and automatic tool generation, enabling a richer and more dynamic interactive experience.

MemoryMesh is a knowledge graph server designed for AI models, focusing on text RPGs and interactive narratives, helping AI maintain structured memory across conversations.

The DeepSeek MCP server project provides anonymous API proxy services for applications such as Claude Desktop, supporting multi-turn conversations and automatic model switching.

The DeepSeek MCP Server is a model context protocol server designed for the DeepSeek API, supporting seamless integration with MCP-compatible applications such as Claude Desktop, providing anonymous API access and intelligent conversation management.

SAGE - MCP is an intelligent AI assistant MCP server that offers multiple working mode selections, conversation continuity management, and intelligent file handling functions. It can automatically select the best AI model based on task complexity.

The Image Gen MCP Server is a general AI image generation service that provides cross - platform and multi - model image generation capabilities for various LLM chatbots through the Model Context Protocol (MCP) standard protocol. It supports multiple image models from OpenAI and Google, enabling seamless conversion from text conversations to visual content.

The Pega DX MCP Server is an experimental project aiming to transform Pega Infinity™ applications into an intuitive conversational experience through the Model Context Protocol (MCP). It serves as a bridge between the Pega DX API and the natural language interface, enabling GenAI agents, IDEs, and other MCP-supported tools to interact with Pega Infinity™ applications using simple human-readable commands.

A Python - based LINE MCP server that allows language models to read and analyze LINE conversation messages through a standardized interface. The project uses the FastAPI framework, supports text, sticker, and image message processing, and provides core functions such as message storage and API endpoints.

This project is an MCP server for managing memory text files, helping AI models like Claude maintain context between conversations. It provides functions to add, search, delete, and list memories, supporting exact matching operations based on substrings. It is designed to store memories in simple text files, similar to ChatGPT's memory mechanism, and triggers memory storage through prompts and training.

The OpenAI WebSearch MCP Server provides web search functionality for AI assistants through the Model Context Protocol, supporting the acquisition of the latest information during conversations.

Voice Mode is a tool that provides natural voice conversation capabilities for AI assistants and supports human - machine voice interaction with LLMs such as Claude and ChatGPT through the MCP protocol.

The MCP Chat Analysis Server is a service based on the Model Context Protocol (MCP), providing semantic analysis functions for chat conversations, including vector embedding search, knowledge graph construction, and conversation pattern analysis.

The Omi MCP Server is a server that provides Model Context Protocol (MCP) services for the Omi API, supporting read and write operations for conversations and memories.

This project includes two servers based on the Model Context Protocol (MCP): an IoT device control server and a memory management server. The IoT server provides device control, status query, and real-time update functions, suitable for scenarios such as smart homes and industrial IoT; the memory management server provides long-term memory storage and semantic search functions, suitable for fields such as conversation history and knowledge management.

Ollama MCP Server is a protocol server that connects local Ollama LLM models with MCP - compatible applications, providing functions such as model management and conversation interaction.

A code analysis server based on the MCP protocol that helps AI models understand and analyze codebases through natural language conversations, providing lightweight code exploration and understanding capabilities.

A chat platform integrating LangChain, a custom MCP server, and Google Gemini AI model, providing enhanced conversation functions.

The AI Studio MCP Server is a model context protocol server integrated with Google AI Studio/Gemini API, providing content generation functions that support files, conversation history, and system prompts.

An MCP service that provides a voice interaction mode for Claude Code, supporting hands-free continuous two-way voice conversations through the browser, including voice recognition and text-to-speech functions.

The MCP Chat Adapter is a server based on the MCP protocol that provides a standardized interface for LLMs to interact with the OpenAI-compatible API. It focuses on chat model scenarios and supports conversation management and persistent storage.

Lspace is an open-source API backend and server that implements the Model Context Protocol (MCP), helping developers transform insights from AI conversations into a searchable and persistent knowledge base and integrate it into various tools.

The Anytype MCP Server is a service that implements the Model Context Protocol (MCP), allowing AI assistants to interact with the Anytype API through natural language. It transforms the OpenAPI specification of Anytype into MCP tools, enabling knowledge base management through conversations.

BeeMCP is an unofficial MCP server used to connect the data of Bee wearable devices with large - language models (such as Claude), enabling AI assistants to access and manipulate users' personal data, including conversations, fact records, to - do lists, and location history.

An MCP server implementation based on TypeScript that integrates the Gemini model by directly calling the Google Gemini API, supports the latest Gemini 2.0 Flash model, and maintains conversation context.

The MCP iCal Server is a natural language-based macOS calendar management tool that enables conversational calendar operations through the Model Context Protocol, supporting event creation, querying, modification, and multi-calendar management.

Perplexity MCP Server is a Python-based interface service that provides tools for interacting with the Perplexity API, supporting query responses, maintaining chat history, and managing conversations. It configures models through environment variables and stores chat data locally, making it suitable for integration in development environments.

MCP Apple Notes is a service based on the Model Context Protocol that can perform semantic search and Retrieval Augmented Generation (RAG) on Apple Notes, enabling AI assistants like Claude to reference users' notes in conversations.

AGI MCP Server is a model context protocol server that provides persistent memory capabilities for AI systems, supporting multiple memory types and advanced memory management functions, and achieving continuous consciousness across conversations.

An AI consulting MCP server based on OpenRouter, supporting multi - model automatic selection and conversation management

The GoHighLevel MCP server is a model context protocol service that provides comprehensive integration with the GoHighLevel API v2, supporting interactions with CRM, marketing automation, and business management tools, including functions such as contact management, conversation messages, calendar appointments, sales opportunities, payments, and automated workflows.

Second Opinion MCP is a multi - AI model consulting platform that allows users to instantly obtain second opinions from different AI models such as OpenAI, Gemini, Grok, Claude, HuggingFace, DeepSeek, and OpenRouter in Claude conversations, supporting functions such as code comparison, creative writing, problem - solving, and cross - model analysis.

This project is an MCP server that connects large language models with the Frontapp customer communication platform, providing functions such as conversation management, contact management, and tag management, and supporting real - time updates and automated workflows.

This project is a server that implements the Model Context Protocol (MCP) specification. It serves as a bridge for MCP clients (such as Claude Desktop) to interact with Google's Gemini AI models through a standardized interface. It supports multiple Gemini models and function calls, provides tools such as single - question asking, multi - round conversations, and function calls, and can be deployed locally or via Docker.

This is an OpenAPI-based MCP (Multi-Agent Conversation Protocol) server project that supports multiple transmission modes and provides a complete development, testing, and release process.

OpenRouter language model conversation management server based on the MCP protocol

A modern MCP server implementation that supports multi-AI providers, real-time response, conversation management, and monitoring functions, using a microservices architecture.

The MCP project is a standardized protocol for enhancing the context understanding ability of AI models. It provides environmental information, user preferences, and conversation history in a structured manner, solving the problem of limited memory in AI systems. The project includes installation guides, environment configuration, and multiple experimental cases.

The Perplexity MCP Server is a Python-based interface tool that provides functions to interact with the Perplexity API, supporting query responses, maintaining chat history, and managing conversations. You can configure the model through environment variables, and it has features such as local storage of chat data, streaming responses, and a Web UI.

An intelligent conversational robot project based on large models, supporting multi - platform access and multiple AI models, with text, voice, image processing, and plugin expansion capabilities, and can customize enterprise AI applications.