mcp-builder

SKILL.md

MCP Server Development Guide

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 properly

Zod schemas with .strict()

TypeScript strict mode enabled

No any types - use proper types

Explicit Promise return types

Build process configured (npm run build)

Phase 3: Review and Refine

After initial implementation:

3.1 Code Quality Review

To ensure quality, review the code for:

DRY Principle: No duplicated code between tools

Composability: Shared logic extracted into functions

Consistency: Similar operations return similar formats

Error Handling: All external calls have error handling

Type Safety: Full type coverage (Python type hints, TypeScript types)

Documentation: Every tool has comprehensive docstrings/descriptions

3.2 Test and Build

Important: MCP servers are long-running processes that wait for requests over stdio/stdin or sse/http. Running them directly in your main process (e.g., python server.py or node dist/index.js) will cause your process to hang indefinitely.

Safe ways to test the server:

Use the evaluation harness (see Phase 4) - recommended approach

Run the server in tmux to keep it outside your main process

Use a timeout when testing: timeout 5s python server.py

For Python:

Verify Python syntax: python -m py_compile your_server.py

Check imports work correctly by reviewing the file

To manually test: Run server in tmux, then test with evaluation harness in main process

Or use the evaluation harness directly (it manages the server for stdio transport)

For Node/TypeScript:

Run npm run build and ensure it completes without errors

Verify dist/index.js is created

To manually test: Run server in tmux, then test with evaluation harness in main process

Or use the evaluation harness directly (it manages the server for stdio transport)

3.3 Use Quality Checklist

To verify implementation quality, load the appropriate checklist from the language-specific guide:

Python: see "Quality Checklist" in 🐍 Python Guide

Node/TypeScript: see "Quality Checklist" in ⚡ TypeScript Guide

Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its effectiveness.

Load ✅ Evaluation Guide for complete evaluation guidelines.

4.1 Understand Evaluation Purpose

Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions.

4.2 Create 10 Evaluation Questions

To create effective evaluations, follow the process outlined in the evaluation guide:

Tool Inspection: List available tools and understand their capabilities

Content Exploration: Use READ-ONLY operations to explore available data

Question Generation: Create 10 complex, realistic questions

Answer Verification: Solve each question yourself to verify answers

4.3 Evaluation Requirements

Each question must be:

Independent: Not dependent on other questions

Read-only: Only non-destructive operations required

Complex: Requiring multiple tool calls and deep exploration

Realistic: Based on real use cases humans would care about

Verifiable: Single, clear answer that can be verified by string comparison

Stable: Answer won't change over time

4.4 Output Format

Create an XML file with this structure:

<evaluation>
  <qa_pair>
    <question>Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?</question>
    <answer>3</answer>
  </qa_pair>
<!-- More qa_pairs... -->
</evaluation>

Reference Files

📚 Documentation Library

Load these resources as needed during development:

Core MCP Documentation (Load First)

MCP Protocol: Fetch from https://modelcontextprotocol.io/llms-full.txt - Complete MCP specification

📋 MCP Best Practices - Universal MCP guidelines including:

Server and tool naming conventions

Response format guidelines (JSON vs Markdown)

Pagination best practices

Character limits and truncation strategies

Tool development guidelines

Security and error handling standards

SDK Documentation (Load During Phase 1/2)

Python SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md

TypeScript SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md

Language-Specific Implementation Guides (Load During Phase 2)

🐍 Python Implementation Guide - Complete Python/FastMCP guide with:

Server initialization patterns

Pydantic model examples

Tool registration with @mcp.tool

Complete working examples

Quality checklist

⚡ TypeScript Implementation Guide - Complete TypeScript guide with:

Project structure

Zod schema patterns

Tool registration with server.registerTool

Complete working examples

Quality checklist

Evaluation Guide (Load During Phase 4)

✅ Evaluation Guide - Complete evaluation creation guide with:

Question creation guidelines

Answer verification strategies

XML format specifications

Example questions and answers

Running an evaluation with the provided scripts