Mcp Builder

What This Skill Does

This skill guides the user in creating high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services. It emphasizes designing tools for AI agents, focusing on workflows, actionable error messages, and natural task subdivisions. This skill is for developers building integrations between LLMs and external systems.

When to Use

Design tools for AI agents. Build MCP servers for LLM integrations. Optimize tools for limited context windows. Create actionable error messages. Follow natural task subdivisions. Evaluate tool quality.

Key Features

Provides agent-centric design principles. Focuses on workflow-based tool creation. Optimizes for limited context windows. Emphasizes actionable error messages. Guides tool naming conventions. Uses evaluation-driven development.

Overview

To create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided.

Process

🚀 High-Level Workflow

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

Phase 1: Deep Research and Planning

1.1 Understand Agent-Centric Design Principles

Before diving into implementation, understand how to design tools for AI agents by reviewing these principles:

Build for Workflows, Not Just API Endpoints: Don't simply wrap existing API endpoints - build thoughtful, high-impact workflow tools Consolidate related operations (e.g., schedule_event that both checks availability and creates event) Focus on tools that enable complete tasks, not just individual API calls Consider what workflows agents actually need to accomplish

Optimize for Limited Context: Agents have constrained context windows - make every token count Return high-signal information, not exhaustive data dumps Provide "concise" vs "detailed" response format options Default to human-readable identifiers over technical codes (names over IDs) Consider the agent's context budget as a scarce resource

Design Actionable Error Messages: Error messages should guide agents toward correct usage patterns Suggest specific next steps: "Try using filter='active_only' to reduce results" Make errors educational, not just diagnostic Help agents learn proper tool usage through clear feedback

Follow Natural Task Subdivisions: Tool names should reflect how humans think about tasks Group related tools with consistent prefixes for discoverability Design tools around natural workflows, not just API structure

Use Evaluation-Driven Development: Create realistic evaluation scenarios early Let agent feedback drive tool improvements Prototype quickly and iterate based on actual agent performance

1.3 Study MCP Protocol Documentation

Fetch the latest MCP protocol documentation:

Use WebFetch to load: https://modelcontextprotocol.io/llms-full.txt

This comprehensive document contains the complete MCP specification and guidelines.

1.4 Study Framework Documentation

Load and read the following reference files:

MCP Best Practices: 📋 View Best Practices - Core guidelines for all MCP servers

For Python implementations, also load: Python SDK Documentation: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md 🐍 Python Implementation Guide - Python-specific best practices and examples

For Node/TypeScript implementations, also load: TypeScript SDK Documentation: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md ⚡ TypeScript Implementation Guide - Node/TypeScript-specific best practices and examples

1.5 Exhaustively Study API Documentation

To integrate a service, read through ALL available API documentation: Official API reference documentation Authentication and authorization requirements Rate limiting and pagination patterns Error responses and status codes Available endpoints and their parameters Data models and schemas

To gather comprehensive information, use web search and the WebFetch tool as needed.

1.6 Create a Comprehensive Implementation Plan

Based on your research, create a detailed plan that includes:

Tool Selection: List the most valuable endpoints/operations to implement Prioritize tools that enable the most common and important use cases Consider which tools work together to enable complex workflows

Shared Utilities and Helpers: Identify common API request patterns Plan pagination helpers Design filtering and formatting utilities Plan error handling strategies

Input/Output Design: Define input validation models (Pydantic for Python, Zod for TypeScript) Design consistent response formats (e.g., JSON or Markdown), and configurable levels of detail (e.g., Detailed or Concise) Plan for large-scale usage (thousands of users/resources) Implement character limits and truncation strategies (e.g., 25,000 tokens)

Error Handling Strategy: Plan graceful failure modes Design clear, actionable, LLM-friendly, natural language error messages which prompt further action Consider rate limiting and timeout scenarios Handle authentication and authorization errors

Phase 2: Implementation

Now that you have a comprehensive plan, begin implementation following language-specific best practices.

2.1 Set Up Project Structure

For Python: Create a single .py file or organize into modules if complex (see 🐍 Python Guide) Use the MCP Python SDK for tool registration Define Pydantic models for input validation

For Node/TypeScript: Create proper project structure (see ⚡ TypeScript Guide) Set up package.json and tsconfig.json Use MCP TypeScript SDK Define Zod schemas for input validation

2.2 Implement Core Infrastructure First

To begin implementation, create shared utilities before implementing tools: API request helper functions Error handling utilities Response formatting functions (JSON and Markdown) Pagination helpers Authentication/token management

2.3 Implement Tools Systematically

For each tool in the plan:

Define Input Schema: Use Pydantic (Python) or Zod (TypeScript) for validation Include proper constraints (min/max length, regex patterns, min/max values, ranges) Provide clear, descriptive field descriptions Include diverse examples in field descriptions

Write Comprehensive Docstrings/Descriptions: One-line summary of what the tool does Detailed explanation of purpose and functionality Explicit parameter types with examples Complete return type schema Usage examples (when to use, when not to use) Error handling documentation, which outlines how to proceed given specific errors

Implement Tool Logic: Use shared utilities to avoid code duplication Follow async/await patterns for all I/O Implement proper error handling Support multiple response formats (JSON and Markdown) Respect pagination parameters Check character limits and truncate appropriately

Add Tool Annotations: readOnlyHint: true (for read-only operations) destructiveHint: false (for non-destructive operations) idempotentHint: true (if repeated calls have same effect) openWorldHint: true (if interacting with external systems)

2.4 Follow Language-Specific Best Practices

At this point, load the appropriate language guide:

For Python: Load 🐍 Python Implementation Guide and ensure the following: Using MCP Python SDK with proper tool registration Pydantic v2 models with model_config Type hints throughout Async/await for all I/O operations Proper imports organization Module-level constants (CHARACTER_LIMIT, API_BASE_URL)

For Node/TypeScript: Load ⚡ TypeScript Implementation Guide and ensure the following: Using server.registerTool