What problem does it solve? Building an MCP server that AI assistants can reliably call requires many decisions—tool naming, schema design, error handling, testing, and deployment—and mistakes in any of them cause tools to be ignored, misused, or crash in production. ## Core Features & Use Cases - Protocol Design Guidance: Clarifies when to use Tools, Resources, or Prompts, with naming, parameter, and description conventions that help AI assistants select tools correctly. - Implementation Patterns: Provides TypeScript and Python project structures, Zod/Pydantic validation, error handling with isError flags, and resource lifecycle management. - Testing & Deployment: Covers unit tests, integration tests via InMemoryTransport, MCP Inspector debugging, and npm/pip/Docker distribution. - Use Case: You want to expose your company's internal API to Claude. Follow the checklist to design snake_case tools with clear descriptions, validate inputs with Zod, test with MCP Inspector, and publish an npm package users configure in their MCP client. ## Quick Start Help me build an MCP server that exposes my service's API as tools for AI assistants, following best practices for design, testing, and deployment.