What problem does it solve?
This Skill removes guesswork when building Model Context Protocol (MCP) servers so LLMs can reliably call tools and complete real workflows against external APIs and services.
Core Features & Use Cases
- Agent-centric tool design: Build tools around complete workflows (not just raw endpoints) with limited-context efficiency in mind.
- Protocol- and SDK-aligned implementation: Use MCP specification guidance plus Python (FastMCP) or Node/TypeScript SDK best practices for schemas, registration, and typing.
- Quality and evaluation loop: Create robust, read-only evaluation tasks that stress tool discovery, pagination, formatting, and error behavior.
Use Case Example: You want an MCP server that lets an agent research documentation, plan actions, and then execute a multi-step integration workflow. Use this Skill to design coherent tool boundaries, consistent schemas, actionable errors, and an evaluation suite that proves the agent can use your tools effectively.
Quick Start
Tell your AI to help you implement an MCP server by following the phases to plan tools, implement core infrastructure and tool logic, then create XML evaluations that verify the server enables realistic read-only Q&A.
