MCP Server Development Guide

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

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Process

Phase 1: Research and Planning

1.1 Agent-Centric Design Principles

Build for Workflows: Create high-impact workflow tools, not API wrappers. Consolidate operations (e.g., schedule_event checks availability AND creates event).

Optimize for Context: Return high-signal info, not dumps. Offer "concise"/"detailed" options. Use human-readable IDs.

Actionable Errors: Guide agents to correct usage: "Try filter='active_only'". Make errors educational.

Natural Subdivisions: Tool names reflect human thinking. Group with consistent prefixes. Design around workflows.

Evaluation-Driven: Create realistic scenarios early. Iterate based on agent performance.

1.2 Study Documentation

Load these in order:

1. MCP Protocol: https://modelcontextprotocol.io/llms-full.txt
2. 📋 MCP Best Practices
3. For Python: https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md + 🐍 Python Guide
4. For Node/TypeScript: https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md + ⚡ TypeScript Guide

1.3 Study API Documentation

Read ALL: API reference, auth, rate limits, pagination, errors, endpoints, parameters, data models. Use web search and WebFetch.

1.4 Create Implementation Plan

Tool Selection: List valuable endpoints. Prioritize common use cases. Consider tool combinations.

Shared Utilities: API request patterns, pagination/filtering/formatting helpers, error handling.

Input/Output: Validation (Pydantic/Zod), consistent formats (JSON/Markdown), detail levels (Detailed/Concise), scale planning, character limits (25k tokens).

Error Handling: Graceful failures, actionable LLM-friendly messages, rate limits, timeouts, auth errors.

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Phase 2: Implementation

2.1 Set Up Project

Python: Single .py or modules. MCP SDK. Pydantic validation.

Node/TypeScript: Project structure (package.json, tsconfig.json). MCP SDK. Zod validation.

2.2 Core Infrastructure First

Create shared utilities: API helpers, error handling, response formatting (JSON/Markdown), pagination, auth/tokens.

2.3 Implement Tools

Input Schema: Pydantic/Zod with constraints (min/max, regex, ranges). Clear descriptions with examples.

Descriptions: Summary, purpose, parameter types with examples, return schema, usage examples, error handling with next steps.

Logic: Use shared utilities (DRY). Async/await for I/O. Error handling. Multiple formats (JSON/Markdown). Respect pagination. Check limits, truncate.

Annotations: readOnlyHint, destructiveHint, idempotentHint, openWorldHint.

2.4 Language-Specific Best Practices

Python (🐍 Guide): MCP SDK, Pydantic v2 with model_config, type hints, async/await, organized imports, constants.

Node/TypeScript (⚡ Guide): server.registerTool, Zod .strict(), strict mode, no any, explicit Promise<T>, build config.

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Phase 3: Review and Refine

3.1 Code Quality

Check: DRY (no duplication), composability (shared logic), consistency (similar formats), error handling (all calls), type safety (full coverage), documentation (comprehensive).

3.2 Test and Build

Important: MCP servers are long-running (stdio/http). Direct runs hang indefinitely.

Safe testing: Evaluation harness (recommended), tmux, or timeout (timeout 5s python server.py).

Python: python -m py_compile your_server.py. Test in tmux or via harness.

Node/TypeScript: npm run build. Verify dist/index.js. Test in tmux or via harness.

3.3 Quality Checklist

See 🐍 Python Guide or ⚡ TypeScript Guide.

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Phase 4: Create Evaluations

Load ✅ Evaluation Guide for complete guidelines.

4.1 Create 10 Questions

Process: Tool inspection → content exploration (READ-ONLY) → question generation → answer verification.

4.2 Requirements

Each question: Independent, read-only, complex (multiple tool calls), realistic, verifiable (single answer), stable.

4.3 Format

<evaluation>
  <qa_pair>
    <question>Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?</question>
    <answer>3</answer>
  </qa_pair>
</evaluation>

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Reference Files

Core: MCP Protocol (https://modelcontextprotocol.io/llms-full.txt), 📋 Best Practices

SDK: Python (https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md), TypeScript (https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md)

Implementation: 🐍 Python, ⚡ TypeScript

Evaluation: ✅ Guide