Cursor Install Guide
AI code editor frequently used with developer-focused MCP servers.
MCP Servers
This is the repository for the LinkedIn Learning course Model Context Protocol (MCP): Hands-On with Agentic AI. The full course is available from LinkedIn Learning.
is a Ruby gem focused on providing Model Context Protocol (MCP) capability to Ruby on Rails applications, specifically as a server.
Supercharge OCaml development with AI coding agents. This MCP server gives AI assistants deep integration with your OCaml projects through Dune, Merlin, and other OCaml Platform tools.
Give AI hands to touch silicon — an MCP server for SEGGER J-Link, OpenOCD and Black Magic probes. Flash, halt, step, stream RTT logs and decode ARM Cortex-M faults. Verified against real hardware.
This project includes code originally developed by Anthropic and released under the MIT License. Substantial modifications and new functionality have been added by For Good AI Inc. (dba Zencoder Inc.
A high-performance Model Context Protocol (MCP) server for Trino implemented in Go. This project enables AI assistants to seamlessly interact with Trino's distributed SQL query engine through...
OpenAPI-MCP is a dockerized MCP server that reads a swagger.json or openapi.yaml file and generates a corresponding Model Context Protocol (MCP) toolset.
Connect BookStack to Claude and other AI assistants through the Model Context Protocol (MCP). This server exposes 56 tools and 11 resources covering the supported subset of the BookStack API — books,...
A list of awesome MCP Gateway Products. Open a pull request to contribute.
The enables AI agents to interact with IBM i systems through the Model Context Protocol (MCP). It provides secure, SQL-based access to Db2 for i databases, allowing AI applications like Claude,...
This is a basic example of a Model Context Protocol (MCP) server implementation that demonstrates core functionality including tools and resources.
MCP In Action 是一个实战项目，旨在帮助开发者快速掌握 Model Context Protocol (MCP) 的开发与应用。本项目通过实际案例展示如何构建基于 MCP 的应用，使大语言模型能够与外部工具和数据源进行交互，从而增强模型的能力范围。
