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moizibnyousaf/ai-agent-skills117 installs

mcp-builder

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).

How do I install this agent skill?

npx skills add https://github.com/moizibnyousaf/ai-agent-skills --skill mcp-builder
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill is a development guide for creating Model Context Protocol (MCP) servers. It provides architectural guidance, best practices, and code snippets for TypeScript and Python implementations. It references official documentation and standard testing tools from the MCP ecosystem. No security risks were identified.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

MCP Server Development Guide

Create MCP servers that enable LLMs to interact with external services through well-designed tools.

High-Level Workflow

Phase 1: Research and Planning

Understand Modern MCP Design:

  • Balance comprehensive API coverage with specialized workflow tools
  • Use clear, descriptive tool names with consistent prefixes (e.g., github_create_issue)
  • Design tools that return focused, relevant data
  • Provide actionable error messages

Study MCP Protocol:

  • Start with sitemap: https://modelcontextprotocol.io/sitemap.xml
  • Key pages: specification, transport mechanisms, tool definitions

Phase 2: Implementation

Recommended Stack:

  • Language: TypeScript (best SDK support)
  • Transport: Streamable HTTP for remote, stdio for local

Project Structure:

my-mcp-server/
├── src/
│   ├── index.ts      # Server entry point
│   ├── tools/        # Tool implementations
│   └── utils/        # Shared utilities
├── package.json
└── tsconfig.json

Tool Implementation Pattern:

server.registerTool({
  name: "github_create_issue",
  description: "Create a new GitHub issue",
  inputSchema: z.object({
    repo: z.string().describe("Repository name (owner/repo)"),
    title: z.string().describe("Issue title"),
    body: z.string().optional().describe("Issue body")
  }),
  outputSchema: z.object({
    id: z.number(),
    url: z.string()
  }),
  annotations: {
    readOnlyHint: false,
    destructiveHint: false,
    idempotentHint: false
  },
  handler: async (input) => {
    // Implementation
    return { id: 123, url: "https://..." };
  }
});

Phase 3: Test

# TypeScript
npm run build
npx @modelcontextprotocol/inspector

# Python
python -m py_compile your_server.py

Phase 4: Create Evaluations

Create 10 complex, realistic questions to test your MCP server:

<evaluation>
  <qa_pair>
    <question>Find all open issues labeled 'bug' in the repo</question>
    <answer>5</answer>
  </qa_pair>
</evaluation>

Tool Design Best Practices

  • Use Zod (TS) or Pydantic (Python) for schemas
  • Include constraints and examples in field descriptions
  • Define outputSchema for structured data
  • Support pagination where applicable
  • Add tool annotations (readOnly, destructive, idempotent)

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/moizibnyousaf/ai-agent-skills/mcp-builder">View mcp-builder on skillZs</a>