The Art of Building Great MCPs
Building good MCPs is an art. You want to minimize wrong tool calls and maximize AI success.Core Principles
1. Less is More
Don’t expose every API endpoint as an MCP tool. Pick the most important ones. ❌ Bad: 50 similar tools confuse AI2. Crystal Clear Descriptions
AI depends on your descriptions to understand when to use tools. ❌ Bad: Vague description3. Simple Schemas
Complex input schemas lead to AI errors. ❌ Bad: Nested complexityTool Design Patterns
Action-Oriented Naming
Use verbs that clearly indicate what happens:Consistent Naming Conventions
Pick a pattern and stick to it:Error-Resistant Parameters
Design parameters that are hard to mess up: ❌ Error-prone: Free text that needs parsingResource Best Practices
Meaningful URIs
Use URIs that make sense to AI: ✅ Good URIs:Clear Resource Descriptions
Explain what data the resource contains: ❌ Vague:Prompt Templates
Use Variables Wisely
Make templates flexible but not complex: ✅ Good template:Testing Your Designs
Test with Realistic Prompts
Don’t just test if tools work - test if AI chooses the right tools:Watch for Confusion Patterns
Common signs AI is confused:- Wrong tool selection: AI picks similar but wrong tool
- Missing parameters: AI doesn’t provide required fields
- Repeated failures: AI keeps trying the same broken approach
- No tool usage: AI gives up and doesn’t use any tools
Common Mistakes
Mistake #1: Too Many Similar Tools
Mistake #2: Boolean Parameters for Actions
Mistake #3: Generic Error Messages
Performance Tips
Optimize for Common Use Cases
Design tools around what users actually ask for: Common request: “Schedule a meeting with John tomorrow at 2pm” ❌ Poor design: Separate tools for each stepMinimize Tool Chains
Long chains of tool calls often fail: ❌ Fragile: 5-step process where any step can fail ✅ Robust: 2-step process with error handlingQuality Checklist
Before deploying your MCP:- Tool names clearly indicate actions
- Descriptions are specific and helpful
- Parameters use simple types when possible
- Similar tools have distinct purposes
- Error messages guide AI toward correct usage
- Resources have meaningful URIs
- Prompts use clear variable names
- Test results show AI picks correct tools
- Success rate is above 80% in testing