This is a comprehensive guide for building MCP servers that actually work well with LLMs. It walks you through research, implementation, testing, and creating evaluations with 10 complex questions to verify your server works. The opinionated take is useful: prioritize comprehensive API coverage over workflow shortcuts, use TypeScript with streamable HTTP for remote servers, and include structured outputs with clear error messages. The four phase structure keeps you from jumping straight to code before understanding the API you're wrapping. Most valuable are the specific patterns for tool design, the evaluation framework requiring realistic multi-step questions, and links to MCP protocol docs and SDK references you'll actually need while building.

GitHub stars
6K
last updated
Jun 11, 2026

Install

$ npx -y skills add thinkinaixyz/deepchat --skill mcp-builder --agent claude-code

Installs into .claude/skills of the current project.

Files

MCP Server Development Guide

Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.


Process

🚀 High-Level Workflow

Creating a high-quality MCP server involves four main phases:

Phase 1: Deep Research and Planning

1.1 Understand Modern MCP Design

API Coverage vs. Workflow Tools: Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.

Tool Naming and Discoverability: Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., github_create_issue, github_list_repos) and action-oriented naming.

Sponsors

Details

Repository
github.com/thinkinaixyz/deepchat
Categories
Documentation, Code Review & Quality, AI & Agent Building, Python
License
Apache-2.0
First seen
Jun 11, 2026

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