Apply — Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers
Apply — Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers
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output_schema
[{"name":"plan","type":"string","description":"Strategic plan or design document"},{"name":"next_steps","type":"array","description":"List of recommended next steps"}]
MCP Server Development Guide
Overview
To create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided.
Process
🚀 High-Level Workflow
Creating a high-quality MCP server involves four main phases:
Phase 1: Deep Research and Planning
1.1 Understand Agent-Centric Design Principles
Before diving into implementation, understand how to design tools for AI agents by reviewing these principles:
Composability: Shared logic extracted into functions
Consistency: Similar operations return similar formats
Error Handling: All external calls have error handling
Type Safety: Full type coverage (Python type hints, TypeScript types)
Documentation: Every tool has comprehensive docstrings/descriptions
3.2 Test and Build
Important: MCP servers are long-running processes that wait for requests over stdio/stdin or sse/http. Running them directly in your main process (e.g., python server.py or node dist/index.js) will cause your process to hang indefinitely.
Safe ways to test the server:
Use the evaluation harness (see Phase 4) - recommended approach
Run the server in tmux to keep it outside your main process
Use a timeout when testing: timeout 5s python server.py
Answer Verification: Solve each question yourself to verify answers
4.3 Evaluation Requirements
Each question must be:
Independent: Not dependent on other questions
Read-only: Only non-destructive operations required
Complex: Requiring multiple tool calls and deep exploration
Realistic: Based on real use cases humans would care about
Verifiable: Single, clear answer that can be verified by string comparison
Stable: Answer won't change over time
4.4 Output Format
Create an XML file with this structure:
<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><!-- More qa_pairs... --></evaluation>
Reference Files
📚 Documentation Library
Load these resources as needed during development:
Core MCP Documentation (Load First)
MCP Protocol: Fetch from https://modelcontextprotocol.io/llms-full.txt - Complete MCP specification
Apply — Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools.
When to Use
Use this skill when building MCP servers
What If Fails
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.
