Skill UI

This guide details the process of building Model Context Protocol (MCP) servers. These servers are crucial for enabling Large Language Models (LLMs) to interact with external APIs and services, facilitating complex, real-world task completion. It provides comprehensive coverage on architectural planning, choosing appropriate languages (Python, TypeScript, C#), and selecting transport mechanisms (HTTP, stdio) for robust integration within enterprise ecosystems like Azure and Foundry.

Used for building, debugging, and extending MCP servers and clients. This skill implements robust connections between AI systems and external tools/data sources using structured protocols like JSON-RPC 2.0. Expertise includes defining tool schemas (Zod/Pydantic), configuring transport layers (HTTP/SSE/stdio), and scaffolding full-stack projects in TypeScript or Python.

Guides plugin developers through integrating Model Context Protocol (MCP) servers into Claude Code, covering configuration files, server types (stdio, SSE, HTTP, WS), environment variables, and authentication so external services appear as structured tools.

This guide details advanced architectural patterns for building complex AI systems using multiple Language Model instances (agents). It addresses the single-agent context bottleneck by distributing work, enabling parallelism, and facilitating specialization across different subtasks. Key patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical designs, providing guidelines for context isolation and effective coordination protocols.

This skill manages the integration of Model Context Protocol (MCP) servers with Windsurf, significantly extending its operational capabilities. It enables Windsurf to interact with external resources, including databases, file systems, APIs, and custom tools through a standardized protocol. Use this when configuring or utilizing advanced, external tool integrations within the Windsurf environment.

A comprehensive guide to establishing production-ready coding standards and context for the Windsurf AI environment. Learn how to utilize root-level rules (.windsurfrules), granular workspace rules, Model Context Protocol (MCP) server integration, and advanced prompt engineering to guide the AI and maintain consistent code quality across projects.

A powerful tool to scaffold and ship production-ready Model Context Protocol (MCP) servers directly from OpenAPI specifications. It supports Python and TypeScript, ensuring schema validation, safe evolution, and robust tool definition generation, eliminating the need for manual wrapper coding. Use it to expose existing REST APIs to LLM agents or build shared tool integrations.

This guide assists developers in designing and architecting Model Context Protocol (MCP) servers that integrate deeply with LLMs like Claude. It is not a coding skill, but a comprehensive consultation process that guides users through use case interrogation, determines the optimal deployment model (e.g., Remote HTTP, MCPB), and selects the appropriate tool-design pattern for seamless, robust integration.

This skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.

This utility automates the creation of a complete, production-ready Model Context Protocol (MCP) server project using Java. It generates full source code templates, including configuration, tool handling, resource definitions, and prompt logic. The generator provides robust setup for both Maven and Gradle, ensuring the resulting backend service is structured, scalable, and ready for deployment with support for reactive streams and optional Spring Boot integration.

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

A command-line interface (CLI) for accessing and interacting with Model Context Protocol (MCP) servers. It acts as a unified gateway, allowing users to discover, inspect, and execute tools and APIs from external sources like filesystems, databases, APIs, and specialized services. Essential for programmatic workflow automation and system integration.