# Tool Integration

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It supports local development and debugging, connection testing through the MCP Inspector, and integration with the Claude desktop application to use mathematical tools.

This project provides two types of MCP servers for accessing functional tools managed by ACI.dev: the apps-server provides access to tools for specified applications, and the unified-server provides functions for discovering and executing all available tools.

MCP is an open-source AI-driven penetration testing framework that integrates multiple security tools, analyzes their outputs in real-time, strictly follows the standard penetration testing process, and provides intelligent assistance to security testers.

ACI.dev is an open-source AI agent tool infrastructure that provides multi-tenant authentication, permission management, and dynamic discovery functions for over 600 pre-integrated tools. It supports calls through a unified MCP Server or a lightweight SDK, simplifying the development of production-level AI agents.

This project provides multiple Model Context Protocol (MCP) services for extending AI assistant capabilities and supports standalone installation and use.

The Gentoro MCP Server is middleware designed for Gentoro services, allowing agents such as Claude to interact with underlying functions through the Gentoro bridge, supporting dynamic integration and management of tools.

This is a Clojure SDK for creating a Model Context Protocol server, providing core components, communication processes, and example implementations, supporting tool calls, resource reading, and prompt retrieval.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It supports local development and debugging, connection testing through the MCP inspector, and integration into the Claude desktop application to call mathematical tools. Finally, a complete process for deploying to Cloudflare is provided.

HydraMCP is a lightweight and scalable network security toolkit that connects AI assistants with security tools through the Model Context Protocol (MCP), supporting AI - assisted security research, scanning, and analysis.

This project sets up an MCP - compatible terminal server on the Windows system to enable communication between external tools and Claude - AI, supporting the execution of Shell commands in the specified workspace.

Documentation for the integration of the Unreal Engine Model Context Protocol (MCP), helping to understand, set up, and use the MCP system.

This project is a setup guide for a Claude - compatible MCP server based on Python and FastMCP, supporting Windows and macOS/Linux systems, providing tool and resource management functions.

Portkey's Model Context Protocol (MCP) server implementation for connecting AI tools and services. Supports Docker, binary, and source code installation, provides SSE and stdio transport modes, and can be integrated with Cursor IDE and Claude Desktop.

An example project of building an MCP server based on Spring AI

Demonstration project for Claude desktop client configuration and tool integration

The Toolhouse MCP Server is an MCP protocol server built on the Toolhouse platform and Groq API, allowing MCP clients to connect and use the Toolhouse tool library to enhance functionality.

Skynet - MCP is a hierarchical AI agent network project based on the Model Context Protocol (MCP), supporting the creation of sub - agents to process complex tasks in parallel, integrating multiple AI models and service tools, and providing functions such as dual - mode operation, tool discovery, and asynchronous execution.

Laravel MCP is a package for implementing the Model Context Protocol (MCP) in Laravel applications. It provides a standardized communication interface between AI models and Laravel applications, supporting multiple transmission methods and rich functional features.

This project demonstrates how to use the MCP protocol to interact with OpenAI, Azure OpenAI, and GitHub models in Azure container applications. It provides a simple terminal application demonstration to interact with the to - do item agent through the toolset provided by the MCP server.

The Ultimate MCP Server is an AI agent operating system based on the Model Context Protocol (MCP), offering dozens of powerful tool capabilities, including cognitive enhancement, tool usage, and intelligent orchestration. The system exposes functions through standardized MCP tools, enabling advanced AI agents to access a rich tool ecosystem and specialized services while optimizing costs, performance, and quality.

A remote MCP server project based on Cloudflare Workers, supporting OAuth login and integrable with MCP Checker and Claude desktop applications.

A production-grade starter template for an MCP server based on TypeScript, providing rapid development tools and a standardized workflow.

This project provides a FastMCP server for batch execution of file and folder operations, including reading, creating, moving, deleting, etc., supporting centralized exception handling and batch tool calls.

The Letta MCP Server is a server toolset that provides agent management, memory operations, and Letta system integration.

JEBMCP is a project that combines JEB and MCP tools, aiming to simplify reverse engineering tasks through Python scripts and provide efficient code analysis and operation functions.

This project is a collection of reference implementations of the Model Context Protocol (MCP), including MCP server implementations for various tools and services, aiming to provide secure and controllable access to tools and data sources for large language models (LLMs).

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.

The MCP Toolkit is a modular server implementation for building high-precision vertical AI agents, providing support for 123 tools, significantly reducing the amount of code, and simplifying complex workflows.

The Model Context Protocol (MCP) is a standardized protocol introduced by Anthropic for the interaction between large language models (LLMs) and external tools and functions, as well as for injecting context into LLMs. It enables models to request real-time information, perform operations in external systems, access professional knowledge bases, and interact with APIs and services. The MCP is similar to a USB hub, serving as a standard interface that reduces the risk of API changes because each MCP server is maintained by its own company, and users only need to integrate it. The MCP server provides three functions: tools, resources, and prompts.

A horizontally scalable implementation of the MCP server