Java-based toolkit delivering scalable performance tests, load benchmarks, results analysis, and cost monitoring for Discovery Intech integrations.

A secure, type-safe MCP server enabling 19 Jira data tools for Server/Data Center with PAT authentication.

Docker Compose toolkit delivering multi-context tooling with automated tests and token-based integrations for GitHub, Docker Hub, Shopify, and Bitbucket.

Zero-friction testing suite with learning mode, dynamic launcher, and interactive tool testing.

Lightweight tester with sample interactions and conformance checks for protocol-based model-context communication.

AI-powered Playwright POM generation with Copilot integration for automated end-to-end testing.

Demonstrates a lightweight, Tavily-backed web search workflow with stdio transport and extensible tooling for rapid model-context experimentation.

Production-ready TypeScript template featuring dual transport, strong validation, and tool-generation for context-aware integrations.

A repository for testing Model Context Protocol (MCP) functionality and features

A lightweight VS Code extension that exposes a single tool to sum three numbers via sequential prompts, running entirely locally.

Demo repository for MCP (Model Context Protocol) examples and experiments

A lightweight toolkit to validate MCP servers, offering scripted tests, mocks, and protocol checks for reliable integrations.

A compact orchestration layer for K6 tests enabling dynamic, scalable context-driven execution with seamless integration.

Python-based implementation enabling standardized browser actions via Selenium, with extensible actions, driver/session management, and a unified tool facade for seamless integration with LLMs and external agents.

Exercise: Integrate Model Context Protocol with GitHub Copilot

MCP（Model Context Protocol）のテスト用リポジトリ

Lightweight test harness validating context-based model interactions with minimal setup and clear traceable flows.

Tutorial implementation of Model Context Protocol server

Model Context Protocol (MCP) is an API interface for your applications that are formatted in a way that machine learning platforms can interact with them. They can be used to generate AI insights, perform tasks based on user input or other things.

A lightweight protocol for centralized model context sharing and collaboration across heterogeneous AI agents, emphasizing efficient context transfer and modular plug-ins.

Beginner-friendly blueprint demonstrating end-to-end Model Context Protocol capabilities with stdio transport, tools, resources, prompts, and Claude integration.

Python-based data explorer with multi-layer caching, rate-limited access, and 16 integrated tooling facets enabling AI-assisted access.

Hands-on examples, tutorials, and reference implementations illustrating practical MCMAP integrations for marketing workflows.

Real-time .NET static analysis inside chat clients with Roslyn-powered type usage, cross-project insights, and safe refactoring validation.