A comprehensive guide to Model Context Protocol (MCP) implementation for GitHub interactions

Tool for transferring Model Context Protocol (MCP) server configurations between Windows systems

A repository demonstrating the Model Context Protocol (MCP) for AI agent interactions

Playground for experimenting with MCP (Model Context Protocol) integrations with Quix

Python SDK atop FastMCP that unifies memory, semantic search, and graph-context across MongoDB, Pinecone, and Neo4j.

Secure, context-aware GitHub MCP agent enabling standardized interactions with repos, issues, PRs, and content management.

Lightweight TypeScript implementation of a Model Context Protocol with stdio transport and a greetings tool for quick prototyping.

A lightweight, Python-based integration enabling Docker daemon management from VS Code via stdio and a configurable docker.sock hookup.

Python-based context provider delivering Jira data to LLMs with structured access and seamless Jira integration.

Lightweight modular toolkit to control Docker, automate GitHub tasks, and boost Claude Desktop workflows with browser automation and Brave Search.

Research and documentation about Model Context Protocol (MCP) debugging tools

MCP(Model Context Protocol) 기술 학습을 위한 실험 환경입니다.

MCP(Model Context Protocol) works with local Ollama-Mistral

Secure, standardized GitHub automation enabling AI assistants to manage repos, issues, PRs, and files with fine-grained permissions and extensible tooling.

A scalable Model Context Protocol implementation for n8n that coordinates contexts across automations, enabling secure, reusable prompts and multi-tenant governance.

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Exercise: Integrate Model Context Protocol with GitHub Copilot

Lightweight, customizable bridge enabling Copilot to leverage dynamic project context via a modular protocol, allowing rapid experimentation without heavy infrastructure.

Seamlessly links Copilot with contextual code guidance, enabling dynamic prompts and shared context to boost accuracy and learning.

Lightweight, extensible bridge that integrates MCP with Copilot for context-aware, prompt-driven code suggestions.

Lightweight Next.js integration enabling seamless model context flow and agent orchestration with minimal boilerplate.