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Update example skills and rename 'artifacts-builder' (#112)

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* Export updated examples

* Rename 'artifacts-builder' to 'web-artifacts-builder'
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2025-11-17 16:34:29 -05:00
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@@ -8,7 +8,7 @@ license: Complete terms in LICENSE.txt
## 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.
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.
---
@@ -20,222 +20,131 @@ Creating a high-quality MCP server involves four main phases:
### Phase 1: Deep Research and Planning
#### 1.1 Understand Agent-Centric Design Principles
#### 1.1 Understand Modern MCP Design
Before diving into implementation, understand how to design tools for AI agents by reviewing these principles:
**API Coverage vs. Workflow Tools:**
Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.
**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
**Tool Naming and Discoverability:**
Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., `github_create_issue`, `github_list_repos`) and action-oriented naming.
**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
**Context Management:**
Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.
**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
**Actionable Error Messages:**
Error messages should guide agents toward solutions with specific suggestions and next steps.
**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
#### 1.2 Study MCP Protocol Documentation
**Use Evaluation-Driven Development:**
- Create realistic evaluation scenarios early
- Let agent feedback drive tool improvements
- Prototype quickly and iterate based on actual agent performance
**Navigate the MCP specification:**
#### 1.3 Study MCP Protocol Documentation
Start with the sitemap to find relevant pages: `https://modelcontextprotocol.io/sitemap.xml`
**Fetch the latest MCP protocol documentation:**
Then fetch specific pages with `.md` suffix for markdown format (e.g., `https://modelcontextprotocol.io/specification/draft.md`).
Use WebFetch to load: `https://modelcontextprotocol.io/llms-full.txt`
Key pages to review:
- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions
This comprehensive document contains the complete MCP specification and guidelines.
#### 1.3 Study Framework Documentation
#### 1.4 Study Framework Documentation
**Recommended stack:**
- **Language**: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code, benefiting from its broad usage, static typing and good linting tools)
- **Transport**: Streamable HTTP for remote servers, using stateless JSON (simpler to scale and maintain, as opposed to stateful sessions and streaming responses). stdio for local servers.
**Load and read the following reference files:**
**Load framework documentation:**
- **MCP Best Practices**: [📋 View Best Practices](./reference/mcp_best_practices.md) - Core guidelines for all MCP servers
- **MCP Best Practices**: [📋 View Best Practices](./reference/mcp_best_practices.md) - Core guidelines
**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](./reference/python_mcp_server.md) - Python-specific best practices and examples
**For TypeScript (recommended):**
- **TypeScript SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns 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](./reference/node_mcp_server.md) - Node/TypeScript-specific best practices and examples
**For Python:**
- **Python SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- [🐍 Python Guide](./reference/python_mcp_server.md) - Python patterns and examples
#### 1.5 Exhaustively Study API Documentation
#### 1.4 Plan Your Implementation
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:
**Understand the API:**
Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.
**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
Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.
---
### 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](./reference/python_mcp_server.md))
- Use the MCP Python SDK for tool registration
- Define Pydantic models for input validation
See language-specific guides for project setup:
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization, dependencies
**For Node/TypeScript:**
- Create proper project structure (see [⚡ TypeScript Guide](./reference/node_mcp_server.md))
- Set up `package.json` and `tsconfig.json`
- Use MCP TypeScript SDK
- Define Zod schemas for input validation
#### 2.2 Implement Core Infrastructure
#### 2.2 Implement Core Infrastructure First
Create shared utilities:
- API client with authentication
- Error handling helpers
- Response formatting (JSON/Markdown)
- Pagination support
**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
#### 2.3 Implement Tools Systematically
For each tool:
For each tool in the plan:
**Input Schema:**
- Use Zod (TypeScript) or Pydantic (Python)
- Include constraints and clear descriptions
- Add examples in field descriptions
**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
**Output Schema:**
- Define `outputSchema` where possible for structured data
- Use `structuredContent` in tool responses (TypeScript SDK feature)
- Helps clients understand and process tool outputs
**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
**Tool Description:**
- Concise summary of functionality
- Parameter descriptions
- Return type schema
**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
**Implementation:**
- Async/await for I/O operations
- Proper error handling with actionable messages
- Support pagination where applicable
- Return both text content and structured data when using modern SDKs
**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](./reference/python_mcp_server.md) 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](./reference/node_mcp_server.md) and ensure the following:**
- Using `server.registerTool` properly
- Zod schemas with `.strict()`
- TypeScript strict mode enabled
- No `any` types - use proper types
- Explicit Promise<T> return types
- Build process configured (`npm run build`)
**Annotations:**
- `readOnlyHint`: true/false
- `destructiveHint`: true/false
- `idempotentHint`: true/false
- `openWorldHint`: true/false
---
### Phase 3: Review and Refine
### Phase 3: Review and Test
After initial implementation:
#### 3.1 Code Quality
#### 3.1 Code Quality Review
Review for:
- No duplicated code (DRY principle)
- Consistent error handling
- Full type coverage
- Clear tool descriptions
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 Build and Test
#### 3.2 Test and Build
**TypeScript:**
- Run `npm run build` to verify compilation
- Test with MCP Inspector: `npx @modelcontextprotocol/inspector`
**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.
**Python:**
- Verify syntax: `python -m py_compile your_server.py`
- Test with MCP Inspector
**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](./reference/python_mcp_server.md)
- Node/TypeScript: see "Quality Checklist" in [⚡ TypeScript Guide](./reference/node_mcp_server.md)
See language-specific guides for detailed testing approaches and quality checklists.
---
@@ -247,7 +156,7 @@ After implementing your MCP server, create comprehensive evaluations to test its
#### 4.1 Understand Evaluation Purpose
Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions.
Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.
#### 4.2 Create 10 Evaluation Questions
@@ -260,7 +169,7 @@ To create effective evaluations, follow the process outlined in the evaluation g
#### 4.3 Evaluation Requirements
Each question must be:
Ensure each question is:
- **Independent**: Not dependent on other questions
- **Read-only**: Only non-destructive operations required
- **Complex**: Requiring multiple tool calls and deep exploration
@@ -291,13 +200,12 @@ Create an XML file with this structure:
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 Protocol**: Start with sitemap at `https://modelcontextprotocol.io/sitemap.xml`, then fetch specific pages with `.md` suffix
- [📋 MCP Best Practices](./reference/mcp_best_practices.md) - 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
- Transport selection (streamable HTTP vs stdio)
- Security and error handling standards
### SDK Documentation (Load During Phase 1/2)