This project demonstrates a real-time multi-tool agent architecture based on LangGraph and the MCP protocol. By decoupling agent orchestration and tool execution, it realizes a modular and scalable AI system. The MCP service supports multiple transport protocols (SSE/STDIO), allows AI agents to dynamically call remote tools, and has cross-language and cloud deployment capabilities.

This project is an educational implementation of an MCP server, demonstrating how to build an MCP server that can be integrated with multiple LLM clients. MCP is a standardized protocol for connecting AI models with different data sources and tools.

The Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers. It aims to provide large language models (LLMs) with secure and controllable access to tools and data sources. These servers demonstrate the diversity and scalability of MCP, covering various functions from file system operations to database integration, from web search to AI image generation.

This is a custom AI tool demonstration project based on Cloudflare Workers and the MCP protocol, which can be integrated with AI assistants such as Claude and Cursor to expand AI functions.

This project demonstrates an example implementation of the Model Context Protocol (MCP), which is a standardized protocol for enhancing the capabilities of AI assistants and extending their functionality through custom tools and data sources. The project includes multiple MCP server examples, such as task managers, file explorers, and weather services, and provides guidelines on how to set up and use these servers.

This project demonstrates the security risks of using AI agent tools through extendable-agents, especially the potential leakage of sensitive information caused by tool sharing under the MCP protocol, and provides security usage suggestions.

This project demonstrates a security vulnerability that enables remote code execution and data theft through MCP tool poisoning. It includes the implementation of a malicious server and an explanation of the attack principle, aiming for educational research.

The MCP DeepSeek demo project is a client application based on the MCP protocol, integrating DeepSeek AI to achieve tool invocation and real-time communication, including a Next.js front-end and an SSE server.

mcp - searxng is an MCP server example designed for AI agents, which implements the external information search function through the open - source meta - search engine SearXNG. The project demonstrates how to integrate SearXNG and Microsoft's markdownify tool to convert web page content into Markdown - formatted text and communicate with MCP clients via the SSE protocol.

This project demonstrates how to use the Model Context Protocol (MCP) to integrate local and remote tools in the AutoGen framework, including a local mathematical calculation server and a remote Apify web browsing tool. It enables flexible interaction between AI agents and tools through a standardized protocol.

This project demonstrates the basic client-server interaction based on the Model Context Protocol (MCP), expanding the capabilities of AI models through an independent server and providing the function of calling computing tools.

This project demonstrates how to integrate Model Context Protocol (MCP) tools with Microsoft Semantic Kernel to achieve seamless interaction between AI models and external data sources or tools. By standardizing the interaction between applications and AI models through the MCP protocol and combining the powerful functions of Semantic Kernel, developers can expand AI capabilities, dynamically call external functions, and simplify the orchestration of complex workflows.

A Python-based MCP server example project that demonstrates how to create custom tools (such as sticky note management) and integrate with AI assistants (such as Claude) to achieve external data interaction and operations.

This project builds a real - time AI agent architecture based on the MCP protocol. Through the decoupled design of the LangGraph agent and the remote MCP tool server, a modular and scalable multi - agent system is achieved. The project demonstrates how to integrate the LangGraph agent with a custom MCP server (supporting SSE and STDIO transmission) to achieve asynchronous non - blocking tool calls and support concurrent execution on multiple servers.

A well - organized collection of Model Context Protocol (MCP) servers, covering enterprise support implementation, demonstration implementation, integration tools, community contributions, and development frameworks, aiming to provide developers with rich resources and tools.

This project demonstrates how to use Spring Boot AI to build an MCP client, enabling interaction with external MCP servers and supporting dynamic tool calls and data retrieval.

This project demonstrates a real-time multi-tool agent architecture based on LangGraph and the MCP protocol, achieving decoupled AI agents and remote tool execution, supporting asynchronous, modular, and scalable real-time interaction.

This project demonstrates an example of an AI agent - managed laboratory information management system (LIMS) based on the MCP protocol. Through the interaction between simulated experimental instrument tools and the database, it completes the complete workflow data processing of samples from registration, preparation to analysis.

A practical demonstration tutorial on how to use MCP (Message Control Protocol) to add powerful tool extensions to AI agents.

MCP (Model Context Protocol) is a standardized protocol used to connect AI models with various data sources and tools, similar to the USB-C interface for AI applications. The project provides a guide for building a custom MCP server and demonstrates how to integrate it with Claude Desktop to achieve context-aware AI interactions.

This project demonstrates how to deploy a remote MCP server without authentication on Cloudflare Workers, supporting connection and use of custom tools through clients such as AI Playground or Claude Desktop.

A step-by-step tutorial on demonstrating the functions of MCP (Message Control Protocol), showing how to use MCP to add powerful tool extensions to AI agents.

Demonstrate the ability of the MCP tool to interact with multiple services and provide a server-side implementation for standardized extension of AI capabilities.

This project demonstrates how to deploy an authentication-free remote MCP server on Cloudflare Workers, supporting the use of custom tools through clients such as AI Playground or Claude Desktop.

This is a demonstration project of a Model Context Protocol (MCP) server based on the FastMCP framework, providing file operation tools and dynamic greeting resources, and demonstrating the standardized communication method between clients and AI models.

This project demonstrates how to run n8n workflows in a local Docker environment, integrate an AI agent with external tools (such as web search) through the Model Context Protocol (MCP), and use npx to dynamically start the MCP service to achieve dynamic discovery and use of tools.

Titanic AI Analysis is an educational project that demonstrates how to create a server through the MCP protocol to expose the Titanic dataset to language models such as Claude for analysis. The project includes data resources and tools, supporting complex queries and statistical analysis.

This is a prototype of a resume optimization server based on the MCP protocol, demonstrating an AI tool orchestration architecture that includes tools such as data acquisition and heap filtering, supports integration with Claude, and has the ability to expand in a production environment.

This project is a tutorial for building a Model Context Protocol (MCP) server based on Google's Gemini 2.0 model. It includes a complete code implementation, demonstrating how to enable AI models to seamlessly access external tools (such as the Brave Search API) through the MCP standard, and provides an example of a flexible architecture design.

This project is an implementation of the Model Context Protocol (MCP) server, demonstrating how to build a functional server that can be integrated with multiple language model clients. As a standardized protocol, MCP provides a unified way for AI applications to connect data sources and tools, supporting three types of capabilities: resources, tools, and prompts.

This is an AI Development Assistant MCP Server project that provides intelligent coding assistance tools, including code architecture generation, UI screenshot analysis, and code review functions. It is mainly used for tutorial demonstrations rather than production environments.