
This is a comprehensive guide for building MCP servers that let LLMs interact with external APIs and services. It walks you through the full process: researching the MCP protocol and your target API, implementing tools in TypeScript or Python, and creating evaluations to test if LLMs can actually use what you built. The opinionated parts are helpful, like recommending TypeScript with streamable HTTP for most cases, and emphasizing comprehensive API coverage over workflow shortcuts. The evaluation section is solid, it makes you verify answers yourself before claiming your server works. If you're building an MCP integration and want more than just the official docs, this gives you a structured playbook with specific testing strategies.
$ npx -y skills add aiskillstore/marketplace --skill mcp-builder --agent claude-codeInstalls into .claude/skills of the current project.
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.
Creating a high-quality MCP server involves four main phases:
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.
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