# AI Tools

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function. It provides a complete process guide for local development, MCP inspector connection, Claude desktop application integration, and cloud deployment.

A server based on the Model Context Protocol (MCP) and Server-Sent Events (SSE), integrating the Brave Search API to provide real-time streaming search functionality for AI models and clients.

A powerful implementation of the Model Context Protocol (MCP) server that integrates multiple tools such as GitLab, Jira, Confluence, and YouTube, providing AI - driven search capabilities and development workflow tools.

An intelligent chatbot based on Streamlit that uses GPT-4o to automatically route user requests to different tools (such as chatting, image generation, database queries, voice synthesis, etc.), supporting rapid experimentation with AI tool routing functions.

A server based on the Model Context Protocol (MCP) that provides resource query and search functions for the Heroicons icon library. It supports two operating modes: HTTP and stdio, facilitating integration and use by AI assistants and proxy applications.

Perplexity Deep Research MCP is a Model Context Protocol server that provides web search functionality for the Perplexity AI API, supporting AI assistants to perform web searches using the sonar - deep - research model.

The LangChain MCP client is a project that demonstrates how to use MCP server tools through the LangChain ReAct agent. It supports flexible selection of LLM models and enables dynamic interaction via the CLI.

A cryptographically secure random number generation server based on the Model Context Protocol (MCP), providing functions for generating various random data types, suitable for AI applications and systems requiring high - quality random numbers.

MCP.science is an open - source project that provides a variety of MCP servers designed specifically for scientific research applications, enabling AI models to interact with scientific data, tools, and resources through a standardized protocol.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality. It supports local development and debugging, and the service can be connected via the MCP inspector or the Claude desktop application, and finally deployed to the Cloudflare cloud.

The Akash MCP Server is a Model Context Protocol (MCP) server implemented in TypeScript, used to connect AI agents and the Akash decentralized cloud network.

A search service implementation based on the MCP protocol, supporting multiple search engines and providing content acquisition capabilities for AI tools.

openai - tool2mcp is an open - source bridging tool that encapsulates OpenAI's built - in high - quality tools (such as web search and code interpreter) into an MCP protocol server, enabling MCP - compatible models like Claude to use these powerful functions.

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 allows the Claude desktop client to call the MCP service. Finally, it provides a complete process for deployment to Cloudflare.

CodeDox is a powerful document and code extraction and search system that supports crawling document websites, extracting code snippets, and providing a fast search function through the MCP protocol.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement the OAuth login function, supporting connection and use through the MCP checker and Claude desktop client.

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 via the MCP Inspector, and allows the Claude desktop client to call the MCP service.

CodeBox-AI is a secure Python code execution service that provides self-hosted code interpreter functionality, supports conversational code execution, dynamic package installation, and visual output, and integrates the MCP protocol for interaction with LLM applications.

The promptz.dev MCP server provides the function of directly accessing prompts on the promptz.dev platform from an AI assistant, reducing context switching in development.

The OpenCV MCP Server is a Python - based computer vision service that provides OpenCV's image and video processing capabilities through the Model Context Protocol (MCP). It offers a full set of computer vision tools from basic image processing to advanced object detection for AI assistants and language models, including image processing, edge detection, face recognition, video analysis, and real - time object tracking.

This project demonstrates how to deploy a remote MCP server on Cloudflare Workers and implement OAuth login functionality, supporting connection and use through the MCP inspector and the Claude desktop application.

This project implements a GitHub code search tool through the Model Context Protocol, providing a standardized external service interaction interface for AI assistants, including functions such as code search and repository viewing.

hyper-mcp is a high-performance MCP server based on WebAssembly plugins, supporting plugin distribution through container registries and suitable for various AI application scenarios.

MCP Server for Spanner is a protocol server for connecting to Google Cloud Spanner databases, supporting the execution of SQL queries, data updates, and table structure operations. It is mainly used for the interaction between AI assistants and databases.

A NestJS module for easily exposing AI tools, resources, and prompts via the Model Context Protocol (MCP), supporting multiple transport types and automatic discovery and registration functions.

A NestJS module for easily exposing AI tools, resources, and prompts via the Model Context Protocol (MCP), supporting multiple transport types and dependency injection.

Model Context Protocol (MCP) is an open - source protocol that provides a series of reference implementations and community - developed servers, enabling large language models (LLMs) to securely and controllably access tools and data sources. The project includes server implementations with various functions, such as file system operations, database access, Git management, Slack integration, etc., and supports TypeScript and Python SDKs.

MCPlanManager is a task management system designed for AI Agents, supporting the MCP standard and providing functions such as task plan initialization, execution, persistence, and visualization. It supports two deployment methods: uvx and Docker.

This is an MCP server for accessing Federal Universe Enhancement Proposal (FEP) documents. By cloning the FEP code repository and exposing tools and resources, it enables AI assistants to read and search FEP documents, solving the access restriction problem caused by Codeberg's anti - AI protection.

3D-MCP is a general model context protocol implementation for 3D software, enabling LLMs to interact across platforms such as Blender, Maya, and Unreal through a unified TypeScript interface.