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Connectors (MCP)48k · #3574

mcp-tool-developer

Creates tools for AI to interact with the world. Develops and publishes these tools completely.

What it does

This tool helps build servers and tools for AI, following a protocol called MCP. It covers the entire process, from initial concept to publishing, using languages like TypeScript and Python.

When to use it

  • ✓When building a new MCP server from scratch.
  • ✓When turning an existing API into an MCP tool.
  • ✓When publishing an MCP server to a registry.

Example of what to ask your AI

“Create an MCP server in TypeScript that has a tool to greet users.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/mcp-tool-developer/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/mcp-tool-developer/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

First, you define what the server will do (tools, data, text templates) and how it will communicate. Then, you design how tools will receive and return information, and finally, write the code using the official development kit.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill mcp-tool-developer

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/mcp-tool-developer/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

MCP Tool Developer

Overview

Expert at building Model Context Protocol (MCP) servers that give AI agents new capabilities. Covers the full MCP development lifecycle: specification, implementation, testing, deployment, and registry publishing. Supports both TypeScript and Python with production-ready patterns.

This skill understands MCP specification primitives (tools, resources, prompts, sampling), transport options (stdio, SSE, Streamable HTTP), and the tool design patterns that make MCP servers reliable and composable.

When to Use This Skill

  • Use when building a new MCP server from scratch
  • Use when wrapping an existing API as an MCP tool
  • Use when debugging MCP server issues
  • Use when designing the tool schema for an MCP server
  • Use when publishing an MCP server to a registry

How It Works

Step 1: Define the MCP Server Scope

Identify what capabilities the server should expose:

  • Tools - Functions the LLM can call (primary use case)
  • Resources - Data the LLM can read (files, APIs, databases)
  • Prompts - Reusable prompt templates

Choose the transport:

  • stdio - For local CLI tools (Claude Code, Cursor)
  • SSE (Server-Sent Events) - For remote/hosted tools
  • Streamable HTTP - New in MCP spec for modern deployments

Step 2: Design the Tool Schema

Define input/output schemas before writing implementation:

{
  name: "tool_name",
  description: "What this tool does (visible to the LLM)",
  inputSchema: {
    type: "object",
    properties: { ... },
    required: [ ... ]
  }
}

Step 3: Implement the Server

Create the server with proper error handling, validation, and logging. Use the official MCP SDK for TypeScript (@modelcontextprotocol/sdk) or Python (mcp).

Step 4: Test and Deploy

Test with the MCP Inspector, validate tool schemas, handle edge cases, then deploy locally or remotely.

Examples

Example 1: TypeScript MCP Server

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({ name: "my-tools", version: "1.0.0" });

server.tool("greet", "Greet someone by name",
  { name: z.string().describe("Person's name") },
  async ({ name }) => ({ content: [{ type: "text", text: `Hello, ${name}!` }] })
);

const transport = new StdioServerTransport();
await server.connect(transport);

Example 2: API Wrapper Pattern

Wrap an external API as an MCP tool with auth, rate limiting, and error handling:

  • Map API endpoints to tools
  • Handle auth via environment variables
  • Transform API responses to LLM-friendly format
  • Add retry logic with exponential backoff

Best Practices

  • Build small, focused tools that can be chained rather than monolithic tools
  • Return structured errors, not crashes - tools should fail gracefully
  • Define schemas before implementation
  • Include descriptions that help the LLM understand when and how to use each tool
  • Validate all inputs against the schema
  • Add rate limiting for external API calls
  • Use environment variables for secrets, never hardcode credentials

Limitations

  • This skill provides guidance and code generation; actual runtime testing requires a development environment
  • MCP specification is evolving; always check the latest spec version
  • Security review is essential before deploying tools that handle sensitive data

Security and Safety Notes

  • Never hardcode API keys or credentials in tool implementations
  • Use environment variables or secret managers for all authentication
  • Validate and sanitize all inputs to prevent injection attacks
  • Rate limit external API calls to prevent abuse
  • Review tool permissions carefully - tools can access files, networks, and execute code

Common Pitfalls

  • Problem: LLM calls tools with wrong parameters Solution: Improve tool descriptions and add examples in the description field. The LLM reads descriptions to decide how to call tools.

  • Problem: Tool times out on large inputs Solution: Add input size validation and pagination. Stream large responses instead of buffering.

Related Skills

  • api-integration-architect - For API design patterns used in MCP tools
  • security-audit-code-reviewer - For reviewing MCP server code security
FAQ

What is MCP?

MCP (Model Context Protocol) is a protocol that allows AI agents to use tools and access data in a structured way.

What languages are supported?

TypeScript and Python are the main languages supported for building MCP servers and tools.

How does the AI know when to use a tool?

The AI uses the descriptions you provide for each tool to understand what it does and when it's appropriate to call it.

Details

Original description by the author

Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#3574
License
MIT
Updated
10/15/2026

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