HiveChat is an AI chat application designed for small and medium - sized teams. It supports multiple large - model service providers and provides functions such as group management, Token limits, and third - party login.

LLM Context is a tool that helps developers quickly inject code/text project content into the chat interface of large language models, supporting intelligent file selection and multiple integration methods.

A generative AI support plugin for Unreal Engine that integrates multiple cutting - edge LLM/GenAI model APIs, providing AI integration layer support in game development, including chat and structured output functions for models such as OpenAI, Claude, and Deepseek, and supporting the Model Control Protocol (MCP) to achieve advanced functions such as scene object control and blueprint generation.

Notebook Intelligence (NBI) is an AI coding assistant and extensible AI framework designed for JupyterLab. It supports models from GitHub Copilot and other LLM providers, including local Ollama models. It significantly enhances productivity through features such as code generation, auto - completion, and chat interfaces, and supports the integration of Model Context Protocol (MCP) services.

mcp-chat is an open-source and general-purpose MCP client tool for testing and evaluating MCP servers and proxies. It supports command-line interaction and web mode, can connect to various MCP servers (JS/Python/Docker), and provides functions such as chat history recording, model selection, and system prompt customization to help developers debug MCP services.

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.

Deep-Co is a cross-platform chat client developed based on Compose Multiplatform. It supports multiple LLM API providers, including OpenRouter, Anthropic, Grok, OpenAI, etc., and is compatible with OpenAI API and local models. It has functions such as chat record management, prompt management, role adaptation, TTS, etc., and supports multiple languages and theme customization.

mcp - scaffold is a development sandbox for verifying the Model Context Protocol (MCP) server, providing support for local LLMs (such as LLaMA 7B) and cloud inference, including a chat interface and reference architecture.

ResearchMCP is a research tool that combines the Model Context Protocol (MCP) with multiple search engines, aiming to provide research capabilities similar to ChatGPT DeepResearch.

Ollama Chat with MCP is a project that demonstrates how to integrate a local language model with real-time web search functionality through the Model Context Protocol (MCP). It includes an MCP web search server, a terminal client, and a Gradio-based web front-end, enabling locally run LLMs to access external tools and data sources.

A customized version of NextChat that creates and deploys MCP servers through chat interactions, integrates LLM models from OpenRouter, and provides functions such as tool extraction, one-click deployment, and AI system integration guides.

This project implements an MCP server for interacting with the Wolfram Alpha API, enabling chat applications to perform calculation queries and obtain structured knowledge. It includes an MCP client example based on Gemini, demonstrating how to connect a large - language model to the MCP server for real - time interaction with the Wolfram Alpha knowledge engine.

A sample chat application demonstrating integration with the Model Context Protocol (MCP) server

WebScout MCP is a web application reverse-engineering tool based on the Model Context Protocol, focusing on automated analysis of chat interfaces and streaming APIs, providing browser automation, network traffic capture, and intelligent pattern detection capabilities

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.

Tome is a MacOS application (support for Windows and Linux is coming soon) developed by the Runebook team. It aims to simplify the use of local large language models (LLMs) with MCP servers. By integrating Ollama and managing MCP servers, users can quickly start chatting with MCP-powered models without dealing with complex configurations.

This project demonstrates how to create an MCP server using Azure Functions and interact through the proxy mode of GitHub Copilot Chat. It includes configuration methods for local and remote MCP servers, as well as a guide for deploying Azure Functions via Terraform.

An MCP server designed for ChatGPT's memory function, supporting memory data sharing with other models. Requires environment configuration and application linking for use.

The Prem MCP Server is a Model Context Protocol server designed for Prem AI, supporting seamless integration with MCP - compatible clients such as Claude, and providing functions such as chat completion, RAG support, document management, and template system.

This tutorial demonstrates how to create an MCP server using Python and the official Microsoft MCP SDK, showing how to define tool functions that can be used by LLMs (such as models in ChatGPT or Open WebUI).

Tiny Chat is a chat application developed in Python. It supports installation in development mode and packaged installation, provides a Web interface and database functions, and integrates the MCP service and the RAG service of the OpenAI API.

The DeepSeek MCP Server is an intermediate service used to connect the DeepSeek API with MCP-compatible applications (such as Claude Desktop), supporting various model parameter configurations and chat functions.

The Chatvolt MCP Server is a TypeScript-based application designed to extend the capabilities of AI agents by providing a set of tools to enable them to interact with the Chatvolt platform. This project serves as a bridge between AI models and the Chatvolt API, supporting operations such as managing agents, querying data storage, and handling CRM scenarios, thereby automating complex workflows and providing a natural language interface for the Chatvolt platform.

This project builds an AI system based on Nasdanika capabilities, focusing on operating on resource collections (interconnected models). It describes model elements and their relationships from multiple angles through the 'narrator' processor, and uses embeddings and vector storage to implement semantic search and RAG (Retrieval - Augmented Generation). It also supports the chat completion functions of OpenAI and Ollama.

This project is a Python - based custom MCP (Model Context Protocol) server demonstration, showing how to implement structured communication between clients and servers through a custom protocol. It is suitable for building flexible network applications such as chat rooms or multiplayer games.

An MCP server project integrated with Glean Chat API, providing model context protocol services, supporting standalone operation and debugging mode, and can be integrated into the Cursor development environment.

Overview: The README.md file provides an overview of the Model Context Protocol (MCP) Server, detailing its components and functions. It explains how MCP enables AI automation through a standardized protocol and connects hosts, clients, servers, local data sources, and remote services. The document also describes the evolution from basic AI chats to advanced AI agents capable of performing automated tasks.

DeepChat is a powerful cross - platform AI assistant that supports the deployment of multiple cloud LLMs and local models, and has rich chatbot functions and search extension capabilities.