# MCP server

Services provided by the MCP server for Notion

This project is an experimental platform for MCP servers based on TypeScript, used for learning and testing the integration with Calude Desktop and Cursor IDE. It extends tutorial examples, provides modular design and extensible functions, and is suitable for developers to explore MCP server development.

Fetch-MCP is an MCP server tool for fetching URLs and YouTube video subtitles.

The Freepik Flux AI MCP Server is a service that creates images from text descriptions for Claude Desktop.

AI-driven cloud development project based on AWS MCP server

A simple MCP server based on Perplexity AI, providing web search functionality

A prototype project of an MCP server for running Microsoft 365 CLI commands, supporting testing and integration through the Inspector or VS Code.

This is a boilerplate implementation project for the Model Context Protocol (MCP) server, providing a basic framework for quickly building custom MCP servers and tools, including multiple functional branch examples.

Ahma MCP is a high-performance MCP server that can quickly convert command-line tools into AI agent tools, support asynchronous multi-threaded execution, achieve zero-code integration through JSON configuration, and significantly improve AI work efficiency.

The eBay read-only MCP server developed by CData exposes eBay data as a relational SQL model through the JDBC driver, enabling LLMs to query real-time eBay data through natural language.

A complete MCP server application based on Windows Forms, integrating LLM API calls, a modern chat interface, tool management, and a configuration system. It can serve as a reference for enterprise-level desktop program development.

This is a Swiss-army-knife-style MCP server that allows you to search for and dynamically add other MCP servers through the MCP registry, thereby expanding the available toolset.

The AWX MCP Server is an industry-standard MCP server that connects the AWX/Ansible automation platform to AI tools, enabling AI assistants to manage job templates, start and monitor tasks, and manage inventories and projects using natural language, automating infrastructure workflows.

The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.

The WordPress Trac MCP Server allows AI assistants to directly search and browse all issue tracking systems on WordPress.org and can be configured and used without coding.

An MCP server that provides AI agents with the ability to create and manage node graphs and projects in 'Genshin Impact: Miliastra Wonderland', supporting node graph operations, project management, and document access.

SERP MCP Server is a Google search result crawler server based on the Model Context Protocol. It supports fingerprint rotation, location encoding, and streamlined mode, and can automatically extract data such as organic results and related searches.

Moonbridge is an MCP server that allows multiple AI coding agents (supporting backends such as Kimi, Codex, OpenCode, Gemini) to be generated in parallel from MCP clients such as Claude Code and Cursor, used to try multiple code solutions simultaneously, enabling parallel exploration and task execution at a lower cost.

mcp-nexus is a Multi-Provider Model Context Protocol (MCP) server that integrates the Tavily and Brave Search search APIs, providing a unified tool interface, API key rotation management, a Web management interface, and usage monitoring functions.

A simple MCP server that supports Shell execution and can be deployed via local Ngrok connection or Docker container.

This project is a TypeScript-based experimental platform for MCP servers, used for learning and testing integration with Claude Desktop and Cursor IDE. It extends the basic tutorial functions, providing a modular design and an extensible sandbox environment.

This project is a basic example of how to set up and configure the MCP server, including installing dependencies, building the project, using the MCP inspection tool, and configuring the Claude desktop application to connect to the MCP server.

LiteMCP is a TypeScript framework for elegantly building MCP (Model Context Protocol) servers. It provides the definition of tools, resources, and prompts, supports full - TypeScript development, has built - in logging, error handling, and CLI debugging tools, as well as SSE transmission support.

Learn how to build and deploy a remote MCP server on AWS EC2 for remote use.

This project provides guides and materials for quickly building MCP servers using Cursor's AI capabilities, including detailed construction steps and example API documentation to help users easily create MCP servers.

HumanMCP is a manually operated MCP server project where users need to manually read requests and write responses.

Use the uv tool to create and run a Python project named mcp - server - demo, build a FastMCP server supporting addition functions through the MCP SDK, and configure the client connection.

The ShotGrid MCP Rust client provides a complete implementation of the ShotGrid/Flow Production Tracking REST API and integrates the Model Context Protocol server, enabling large - language models to directly interact with ShotGrid.

An MCP server specifically designed to obtain information from the Guozaoke forum. It allows you to access forum topics and content through an AI assistant, and supports functions such as obtaining the latest topics, today's popular topics, and topic details.

The Athena Protocol is an intelligent MCP server acting as a technical leader for AI programming agents, providing expert verification, impact analysis, and strategic guidance. It helps AI agents detect problems, validate assumptions, and optimize solutions before code changes, thereby improving code quality, reducing regressions, and making more informed architectural decisions.