MCP Server Development Guide

Create high-quality MCP servers that enable LLMs to effectively interact with external services. Quality is measured by how well the server enables LLMs to accomplish real-world tasks.

When to Use

• Building new MCP servers to integrate APIs or services
• Improving existing MCP server tool design
• Understanding agent-centric design principles
• Creating evaluations for MCP servers

Quick Reference

Topic	Reference
Universal guidelines, naming, response formats	references/mcp_best_practices.md
Python/FastMCP implementation	references/python_mcp_server.md
Node/TypeScript implementation	references/node_mcp_server.md
Creating evaluation questions	references/evaluation.md

Agent-Centric Design Principles

Build for Workflows, Not API Endpoints

• Don't simply wrap API endpoints - build workflow tools
• Consolidate related operations (e.g., schedule_event checks availability AND creates event)
• Focus on tools that complete tasks, not individual API calls

Optimize for Limited Context

• Return high-signal information, not exhaustive data dumps
• Provide "concise" vs "detailed" response format options
• Default to human-readable identifiers (names over IDs)
• Treat agent context budget as scarce resource

Design Actionable Error Messages

• Guide agents toward correct usage patterns
• Suggest next steps: "Try using filter='active_only' to reduce results"
• Make errors educational, not just diagnostic

Use Evaluation-Driven Development

• Create realistic evaluation scenarios early
• Let agent feedback drive tool improvements
• Prototype quickly and iterate based on agent performance

Development Workflow

Phase 1: Research and Planning

1. Study MCP Protocol: Fetch https://modelcontextprotocol.io/llms-full.txt
2. Load best practices: Read references/mcp_best_practices.md
3. Study target API: Read ALL available documentation
4. Create implementation plan: Tool selection, shared utilities, I/O design, error handling

Phase 2: Implementation

1. Set up project - Python: single .py or modules; TypeScript: proper project structure
2. Implement infrastructure - API helpers, error handling, response formatting, pagination
3. Implement tools - Input schemas (Pydantic/Zod), docstrings, tool logic, annotations

Load language-specific guide:

• Python: references/python_mcp_server.md
• TypeScript: references/node_mcp_server.md

Phase 3: Review and Refine

1. Code quality review - DRY, composability, consistency, error handling, type safety
2. Test and build - Use evaluation harness or tmux (servers are long-running processes)
3. Quality checklist - See language-specific guide

Phase 4: Create Evaluations

Create 10 complex, realistic questions to test MCP server effectiveness.

Load references/evaluation.md for complete guidelines.

Question requirements:

• Independent, read-only, complex, realistic
• Verifiable with single clear answer
• Stable (won't change over time)

External Resources

• MCP Protocol: https://modelcontextprotocol.io/llms-full.txt
• Python SDK: https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
• TypeScript SDK: https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md