| name | mcp-builder |
| description | Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external agents through well-designed tools. Use when building MCP servers to integrate external APIs or agents, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). |
| license | Complete terms in LICENSE.txt |
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:
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:
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>
</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)
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
