Build MCP servers with FastMCP or TypeScript MCP SDK following official best practices.
npx skills add https://github.com/JetBrains/skills --skill mcp-builder-jetbrains
Please help me install this Agent Skill. Skill: mcp-builder Source: https://github.com/JetBrains/skills/tree/main/mcp-builder Command: npx skills add https://github.com/JetBrains/skills --skill mcp-builder-jetbrains
This Skill provides comprehensive guidance and scaffolding for building robust MCP servers that let LLMs interact with external services through well-designed tools, enabling scalable API integration and automation across Python (FastMCP) and Node/TypeScript (MCP SDK).
Create a new MCP server project using the recommended SDK (Python or TypeScript), then register a simple tool that returns a static message to verify end-to-end tool invocation.
High-intent search queries and answers about installing and using this skill.
To build a robust MCP server, follow official best practices for tool registration, transport selection, and error handling using either the Python FastMCP or TypeScript MCP SDK. Apply architectural guidelines to ensure scalable API integration and validate inputs using Pydantic or Zod.
Best practices for MCP tool registration involve using the MCP SDK to define input and output schemas, validating inputs with Zod or Pydantic, and implementing comprehensive error handling. Responses should support both JSON and Markdown formats with proper pagination.
Yes, you can use Python FastMCP and TypeScript MCP SDK to expose external resources and register tools. These SDKs support building end-to-end MCP workflows that let LLMs interact with external services through well-designed tools and scalable API integration.
Secure an MCP server by validating inputs with Zod or Pydantic and integrating security considerations like authentication and error handling. Ensure tools are registered via the MCP SDK with proper annotations and that response formatting supports JSON and Markdown.
Yes, MCP server scaffolding supports pagination and multiple response formats including JSON and Markdown. It provides step-by-step guidance for implementing core infrastructure, API clients, and response formatting to ensure reliable LLM interactions.
Transport selection and specific naming conventions are needed to ensure reliable MCP server communication and tool discovery. Following these architectural guidelines enables scalable API integration, proper tool registration, and consistent error handling across Python and TypeScript implementations.
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