ClickUp MCP

# ClickUp MCP Server Optimization Project ## Overview This project involves optimizing an **existing ClickUp MCP (Model Context Protocol) server** to make it more efficient, performant, and aligned with MCP best practices. ## Goals We are applying the guidelines and best practices from `mcpguide.md` to improve: 1. **Performance**: Making the server faster and more responsive 2. **Efficiency**: Reducing unnecessary operations and optimizing resource usage 3. **Compliance**: Ensuring full alignment with MCP protocol specifications 4. **Reliability**: Improving error handling and result consistency ## Approach ### Applying MCP Best Practices We're using `mcpguide.md` as our reference guide to: - Implement proper tool schemas with clear input/output definitions - Optimize content types (text, structured, resource links, etc.) - Improve error handling (protocol errors vs. tool execution errors) - Add proper annotations for better context - Implement security best practices ### Direct Tool Testing #### What is Direct Testing? In normal MCP operation, the flow is: ``` Client → JSON-RPC Request → MCP Server → Tool Handler → JSON-RPC Response → Client ``` With **direct testing**, we bypass the protocol layer: ``` Test Script → Tool Handler Function → Direct Result ``` This means we: - Call the actual TypeScript/JavaScript handler functions directly - Pass arguments as native objects instead of JSON-RPC params - Receive raw results without protocol wrapping - Can inspect internal state and intermediate values #### How We're Doing It We're using direct invocation scripts (like `test-tool-direct.js`) that: 1. **Import the tool handlers directly**: ```javascript import { handleTaskSearch } from './src/tools/task/handlers.js'; ``` 2. **Call functions with test parameters**: ```javascript const result = await handleTaskSearch({ query: "test task", limit: 10 }); ``` 3. **Inspect raw outputs** before they're formatted for MCP protocol 4. **Measure performance** of individual operations: - API call latency - Data transformation time - Memory usage patterns - Error handling behavior #### Benefits of Direct Testing This approach allows us to: - **Rapid iteration**: Test changes in seconds instead of full server restarts - **Precise debugging**: Use debuggers, breakpoints, and console logs at the function level - **Performance profiling**: Measure exact execution time without protocol overhead - **Edge case testing**: Easily test malformed inputs, edge cases, and error conditions - **Regression testing**: Build a suite of direct tests to prevent regressions - **Isolation**: Test individual tools without dependencies on the full MCP stack #### Integration Testing After validating improvements via direct testing, we: 1. Verify the tools work correctly through the full MCP protocol 2. Test with actual MCP clients (like Claude Desktop) 3. Ensure JSON-RPC formatting is correct 4. Validate schema compliance and error handling at protocol level ## Reference Documentation ### Key Documents - **`mcpguide.md`**: Official MCP protocol specification and requirements - **`MCP_DESIGN_PRINCIPLES.md`**: AI-first design principles and best practices for building efficient MCP servers - Covers the difference between API-first and AI-first design - Tool consolidation strategies - Performance optimization patterns - Schema design best practices - Real-world examples and anti-patterns ### Design Philosophy The optimization work is guided by the **AI-First mindset** rather than traditional API design: - **User Intent First**: Tools designed around what users naturally ask for, not how the API is structured - **Reduce Complexity**: Fewer, more powerful tools that handle complete use cases - **Natural Language Friendly**: Parameters that match how users think and speak - **Rich Contextual Results**: Return data that helps the AI understand and present information See `MCP_DESIGN_PRINCIPLES.md` for detailed explanations and examples. ## Current Focus Areas 1. Tool schema optimization (applying AI-first design principles) 2. Response format standardization (rich, contextual results) 3. Error handling improvements (clear, actionable messages) 4. Performance profiling and optimization (minimize API calls, optimize caching) 5. Resource usage reduction (consolidate tools, batch operations) 6. Tool consolidation (reduce decision complexity for AI) ## Testing Strategy - Direct tool invocation for unit-level testing - Protocol-compliant testing for integration validation - Performance benchmarking before/after optimizations - Edge case and error scenario validation - AI usability testing (can the AI use tools effectively?)