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mcp-builder-skill 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).
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name MCP Builder Skill description 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). license MIT compatibility opencode
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:
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
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)
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
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
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:
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
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
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:
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)
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 toolsComposability : Shared logic extracted into functionsConsistency : Similar operations return similar formatsError Handling : All external calls have error handlingType 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
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)
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.
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 capabilitiesContent Exploration : Use READ-ONLY operations to explore available dataQuestion Generation : Create 10 complex, realistic questionsAnswer Verification : Solve each question yourself to verify answers
4.3 Evaluation Requirements
Independent : Not dependent on other questionsRead-only : Only non-destructive operations requiredComplex : Requiring multiple tool calls and deep explorationRealistic : Based on real use cases humans would care aboutVerifiable : Single, clear answer that can be verified by string comparisonStable : 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.mdTypeScript 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
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
1.2 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.
1.3 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.mdPython 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.mdTypeScript Implementation Guide - Node/TypeScript-specific best practices and examples
1.4 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.
1.5 Create a Comprehensive Implementation Plan Based on your research, create a detailed plan that includes:
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
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)
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
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
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:
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
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
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
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)
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 toolsComposability : Shared logic extracted into functionsConsistency : Similar operations return similar formatsError Handling : All external calls have error handlingType 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
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)
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.
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 capabilitiesContent Exploration : Use READ-ONLY operations to explore available dataQuestion Generation : Create 10 complex, realistic questionsAnswer Verification : Solve each question yourself to verify answers
4.3 Evaluation Requirements
Independent : Not dependent on other questionsRead-only : Only non-destructive operations requiredComplex : Requiring multiple tool calls and deep explorationRealistic : Based on real use cases humans would care aboutVerifiable : Single, clear answer that can be verified by string comparisonStable : 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 specificationMCP 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.mdTypeScript 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
When to Use This skill is applicable to execute the workflow or actions described in the overview.
