Mcp Builder

MCP Server Development Guide

Overview

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to accomplish real-world tasks through well-designed tools. Quality is measured not by API coverage, but by how effectively agents can use your tools to complete realistic workflows.

Core insight: MCP servers expose tools for AI agents, not human users. Design for agent constraints (limited context, no visual UI, workflow-oriented) rather than human convenience.

When to Use This Skill

Activate when:

• Building MCP servers for external API integration
• Adding tools to existing MCP servers
• Improving MCP server tool design for better agent usability
• Creating evaluations to test MCP server effectiveness
• Debugging why agents struggle with your MCP tools

Language Support:

• Python: FastMCP framework (recommended for rapid development)
• Node/TypeScript: MCP SDK (recommended for production services)

The Iron Law

DESIGN FOR AGENTS, NOT HUMANS

Every tool must optimize for:
- Context efficiency (agents have limited tokens)
- Workflow completion (not just API calls)
- Actionable errors (guide agents to success)
- Natural task subdivision (how agents think)

If your tools are just thin API wrappers, you're violating the Iron Law.

Core Principles

1. Agent-Centric Design First: Study design principles before coding. Tools should enable workflows, not mirror APIs.

2. Research-Driven Planning: Load MCP docs, SDK docs, and exhaustive API documentation before writing code.

3. Evaluation-Based Iteration: Create realistic evaluations early. Let agent feedback drive improvements.

4. Context Optimization: Every response token matters. Default to concise, offer detailed when needed.

5. Actionable Errors: Error messages should teach agents correct usage patterns.

Quick Start

Phase 1: Research and Planning (40% of effort)

1. Study Design Principles: Load design_principles.md to understand agent-centric design 2. Load Protocol Docs: Fetch https://modelcontextprotocol.io/llms-full.txt for MCP specification 3. Study SDK Docs: Load Python or TypeScript SDK documentation from GitHub 4. Study API Exhaustively: Read ALL API documentation, endpoints, authentication, rate limits 5. Create Implementation Plan: Define tools, shared utilities, pagination strategy, error handling

See workflow.md for complete Phase 1 steps.

Phase 2: Implementation (30% of effort)

1. Setup Project: Create structure following language-specific guide 2. Build Shared Utilities: API helpers, error handlers, formatters BEFORE tools 3. Implement Tools: Use Pydantic (Python) or Zod (TypeScript) for validation 4. Follow Best Practices: Load language-specific guide for patterns

See workflow.md for complete Phase 2 steps and language guides.

Phase 3: Review and Refine (15% of effort)

1. Code Quality Review: Check DRY, composability, consistency, type safety 2. Test Build: Verify syntax, imports, build process 3. Quality Checklist: Use language-specific checklist

See workflow.md for complete Phase 3 steps.

Phase 4: Create Evaluations (15% of effort)

1. Understand Purpose: Evaluations test if agents can answer realistic questions using your tools 2. Create 10 Questions: Complex, read-only, independent, verifiable questions 3. Verify Answers: Solve yourself to ensure stability and correctness 4. Run Evaluation: Use provided scripts to test agent effectiveness

See evaluation.md for complete evaluation guidelines.

Navigation

Core Design and Workflow

• [🎯 Design Principles](./reference/design_principles.md) - Agent-centric design philosophy: workflows over APIs, context optimization, actionable errors, natural task subdivision. Read FIRST before implementation.

• [🔄 Complete Workflow](./reference/workflow.md) - Detailed 4-phase development process with step-by-step instructions, decision trees, and when to load each reference file.

Universal MCP Guidelines

• [📋 MCP Best Practices](./reference/mcp_best_practices.md) - Naming conventions, response formats, pagination, character limits, security, tool annotations, error handling. Applies to all MCP servers.

Language-Specific Implementation

• [🐍 Python Implementation](./reference/python_mcp_server.md) - FastMCP patterns, Pydantic validation, async/await, complete examples, quality checklist. Load during Phase 2 for Python servers.

• [⚡ TypeScript Implementation](./reference/node_mcp_server.md) - MCP SDK patterns, Zod validation, project structure, complete examples, quality checklist. Load during Phase 2 for TypeScript servers.

Evaluation and Testing

• [✅ Evaluation Guide](./reference/evaluation.md) - Creating realistic questions, answer verification, XML format, running evaluations, interpreting results. Load during Phase 4.

Key Reminders

• Research First: Spend 40% of time researching before coding
• Agent-Centric: Design for AI workflows, not API completeness
• Context Efficient: Every token counts - default concise, offer detailed
• Actionable Errors: Guide agents to correct usage
• Shared Utilities: Extract common code - avoid duplication
• Evaluation-Driven: Create evals early, iterate based on feedback
• MCP Servers Block: Never run servers directly - use evaluation harness or tmux

Red Flags - STOP

If you catch yourself:

• "Just wrapping these API endpoints directly"
• "Returning all available data fields"
• "Error message just says what failed" (not how to fix)
• Starting implementation without reading design principles
• Coding before loading MCP protocol documentation
• Creating tools without knowing agent use cases
• Skipping evaluation creation
• Running python server.py directly (will hang forever)

ALL of these mean: STOP. Return to design principles and workflow.

Integration with Other Skills

• systematic-debugging: Debug MCP server issues methodically
• test-driven-development: Create failing tests before implementation
• verification-before-completion: Verify build succeeds before claiming completion
• defense-in-depth: Add input validation at multiple layers

Real-World Impact

From MCP server development experience:

• Well-designed servers: 80-90% task completion rate by agents
• API wrapper approach: 30-40% task completion rate
• Context-optimized responses: 3x more information in same token budget
• Actionable errors: 60% reduction in agent retry attempts
• Evaluation-driven iteration: 2-3x improvement in agent success rate

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Remember: The quality of an MCP server is measured by how well it enables LLMs to accomplish realistic tasks, not by how comprehensively it wraps an API.