Agent skill · neolabhq
build-mcp
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
What it needs
About 7k tokens when loaded.
What this skill does
MCP Server Development Guide Overview To create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided. --- Process 🚀 High-Level Workflow Creating a high-quality MCP server involves four main phases: Phase 1: Deep Research and Planning 1.1 Understand Agent-Centric Design Principles Before diving into implementation, understand how to design tools for AI agents by reviewing these principles: Build for Workflows, Not Just API Endpoints: Don't simply wrap existing API endpoints - build thoughtful, high-impact workflow tools Consolidate related operations (e.g., scheduleevent that both checks availability and creates event) Focus on tools that enable complete tasks, not just individual API calls Consider what workflows agents actually need to accomplish Optimize for Limited Context: Agents have constrained context windows - make every token count Return high-signal information, not exhaustive data dumps Provide "concise" vs "detailed" response format options Default to human-readable identifiers over technical codes (names over IDs) Consider the agent's context budget as a scarce resource Design Actionable Error Messages: Error messages should guide agents toward correct usage patterns Suggest specific next steps: "Try using filter='activeonly' to reduce results" Make errors educational, not just diagnostic Help agents learn proper tool usage through clear feedback Follow Natural Task Subdivisions: Tool names should reflect how humans think about tasks Group related tools with consistent prefixes for discoverability Design tools around natural workflows, not just API structure Use Evaluation-Driven Development: Create realistic evaluation scenarios early Let agent feedback drive tool improv …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills neolabhq/build-mcp--72c6e9